Article

Path planning for solar-powered UAV in urban environment

Authors:
  • Beijing Institute of Control Engineering
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Abstract

Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical System (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

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... UAV path planning problems have received great attentions, and various UAVs, such as quadrotor [10], solarpowered UAV [11] and tiltrotor [12] are introduced to execute different tasks in urban environments. According to the complexity of path planning, two categories can be got, i.e., path planning for a single UAV and coordinated path planning for multiple UAVs. ...
... Although drones u and v are both safe at the two positions, they will collide with each other during the travel between B ( # ) and B ( # + 1) (or F ( 2 ) and F ( 2 + 1) ). The mathematical form of checking this conflict is given in Eq. (11). ...
... Eq. (9) presents the constraint for a single drone, and the conflicts between two drones are shown in Eqs. (10) and (11). Actually, the constraint that the distance between two drones cannot be too small at a specific moment is converted to an equivalent constraint that the time interval of passing the same point must be greater than a certain value, as expressed in Eqs. ...
Article
Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of 'AirMatrix', the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multi-path planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization (CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators' requests are both considered. A 'cross-off' strategy is developed to calculate the fitness value, and a 'distributed-centralized' strategy is applied considering the task priorities of drones. A genetic algorithm (GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.
... Additionally, recent advances in the field of automation expanded the use of robotics incomplete area coverage tasks [25,26], like area maintenance (cleaning, painting, inspection) or even construction work (tiling robots) [27,28]. Surveillance, collision avoidance, UAVs, city flights, car traffic regulation, railroad management, mining/demining operations, multi-vehicle systems in workspaces for automated mobility on demand, and automated harvesters are some of the uses of multi-robot systems [29][30][31][32][33][34]. Automated structures' assembly [35] is yet another recent achievement making use of MRS. ...
... -Incorporation of an adaptive Chaos-Gaussian switching strategy into PP framework by [30]. -Self-adaptive learning mechanism incorporated into PSO by [58]. ...
... Wu et al. [30] proposed a path planning framework for UAVs flying in the urban environment, considering not only the static and dynamic obstacles but the shadow regions in the workplace environment as well, for the solar-powered drones. They used a combination of Interfered Fluid Dynamical System (IDFS), another nature-inspired algorithm that mimics the process by which river water is kept evenly away from rocks and eventually reaches its destination. ...
Article
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Recent advances in technology lead to the use of robotic systems as part of the modern working environment. Single and multiple robotic systems work closely with humans to accomplish desired tasks, and the recent advancements have made the usage of multi-robot teams more appealing. One critical problem in utilizing the robot’s full potential is the Path planning problem and, while in the case of a single’s robot, path planning has been extensively investigated, in the case of Multiple Robotic Systems (MRS), especially in dynamic changing environments, there are significant open challenges. Based on the statement mentioned above, a detailed survey has been conducted to highlight these challenges and identify potential solutions. In addition, the beneficial use of MRS is presented, as opposed to single robotic systems through the literature, and already-achievable industry-related results are provided. It is concluded that the practical application of path planning in dynamic environments using MRS is still a field of research and development, requiring the community to engage more with practical applications.
... Path planning of solar powered UAV was discussed in literature. 13 A new type of UAV is gaining fame that combines the best features from UAVs and is know as hybrid UAV. A comprehensive overview of the hybrid UAVs was presented in reference. ...
... In 2D, the UAV will fly at a fixed altitude with constant speed and the direction is not changed periodically, while path planning in 3D is a complex task as there is a changing altitude, UAV changes its speed according to the height, and the direction can be changed at any moment. Commonly used path planning methods focus on 2D, 13 accomplish. However, with the advancement in UAV maneuverability and the demands for low-altitude and terrain following flight, path planning in 3D terrain is gaining more attention. ...
... Obstacles were also modeled as cuboid or cylinder. 13 If the environment is obstacle free, the flight of UAV is usually in straight paths from source to the destination. 64 While flying in a region near radar detection, UAV is required to maintain larger distance from radar so that the probability of being detected is minimum. ...
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With the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.
... Using solar energy, authors design a UAV flight path planning algorithm. 24 The proposed scheme has improved the original algorithm in three aspects. In order to ensure the avoidance of UAV obstacles, the scheme is improved centered on Interfered Fluid Dynamical System (IFDS). ...
... Hosseini et al. 19 Time Wu et al. 24 High reliability with improved accuracy High computational and time complexity. ...
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Aiming to the applications from security surveillance, military operational capabilities to the content and package delivery, unmanned aerial vehicles (UAVs) has a successfully created his space in the available technologies. The compact sized powerful flying robots are wirelessly controlled and are capable to complete tasks with and without direct human intervention. UAVs however still face serious challenges that limit the dream of complete autonomous unmanned flying machines. The key challenges include path planning and obstacle avoidance of these unmanned flying robots that are unavoidable while performing the application‐specific functionalities both in indoor and outdoor environments. In this manuscript with a survey, we investigate the state‐of‐the‐art UAV path planning algorithms and obstacle avoidance techniques. We have also summarized and compared the schemes in tabular form. In addition, current and future research directions and challenges are also discussed, showing the prospective research directions. Unmanned aerial vehicles (UAVs) have a wide range of applications including security surveillance, military operational capabilities, and content and package delivery. Small‐sized wirelessly controlled flying robots complete tasks with and without direct human intervention. UAVs still face serious challenges including path planning and obstacle avoidance that are unavoidable while performing the application‐specific functionalities both in indoor and outdoor environments. In this manuscript with a survey, we investigate the state‐of‐the‐art UAV path planning algorithms and obstacle avoidance techniques.
... Intelligent methods are heuristic algorithms, e.g., Genetic Algorithm (GA) [19], Particle Swarm Optimization (PSO) algorithm [20] and Whale Optimization Algorithm (WOA) [21], etc. These algorithms simulate the process of biological evolution and search for the optimal value in the solution space based on random methods. ...
... Reference [20] applied phase angle-encoded and quantum behaved PSO to three-dimensional route planning for UAVs. Reference [21] introduced a nature-inspired path planning method called interfered fluid dynamical system (IFDS) and proposed a novel intelligent optimization algorithm WOA to plan UAV paths. In general, the above achievements can provide excellent collision free paths, however, they have high computational complexity and cannot yet meet the requirements of UAV real-time control. ...
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The increasing number of Unmanned Aerial Vehicles (UAVs) in the low-altitude airspace and the increasing complexity of the work environment present new challenges for ensuring airspace security, especially the effective conflict detection and resolution (CD&R) of UAVs. In the era of the sixth generation (6G) technology, there is an improvement in communication speed and capacity in comparison with the traditional communication technologies, which contributes to forming a UAV Internet of Things (IoTs) through remote intelligent control platform and improve the effect of CD&R. In this paper, we innovatively develop a cooperative CD&R method in the UAV IoT environment considering UAV relative motion relationships and UAV priorities. Using this method in 6G environment, the real-time and reactive conflict-free paths for UAVs can be generated. The developed method has the advantage of smaller calculation and needs fewer UAVs to take maneuvers than the CD&R methods based on traditional Artificial Potential Field (APF). To verify the effectiveness of CD&R methods, a safety assessment method (evaluate from both conflict feature and network structure perspectives) is also proposed. A Monte Carlo Simulation with "clone mechanism" is designed to incorporate the effect of CD&R systems. Three cases of distributed CD&R protocols are simulated and compared. The simulations with different parameter settings are also discussed. Quantitative simulation experiments show that the safety effect of CD&R proposed in this paper is improved a lot due to the improved APF and the UAV priority determination. Meanwhile, the safety assessment method is demonstrated to be feasible for evaluating the safety of CD&R systems.
... As a large fraction of energy demand comes from cities [5], bringing energy production closer to consumers reduces transmission and distribution losses [6,7] and may make the electrical grid more efficient and adaptable. There has been notable research interest in urban solar harvesting, including solar accessibility in developing cities [4,8], energy implications of shadow distribution on roofs [9], solar potential maps [10] toward more sustainable urban planning [11], energy-efficient architecture [12], solar-powered aerial vehicles [13], fixed building-integrated PV [14][15][16], and dynamic building envelopes [17]. Other complex environments include heliostats [18], step-like fields [19], and windsolar dual land use [20]. ...
... This simplifies energy estimations, especially concerning potentially intricate shadowing effects [6,11], but it may restrain flexibility in environments with challenging constraints [36]. In complex environments, more dynamic tracking [13] and positioning strategies [28] can improve energy efficiency and versatility. For instance, solar modules may be positioned in 3D arrangements [34], in nonregular grids [24,36], or use individualized [35] and context-aware tracking (e.g., backtracking [22]) to minimize shadows and maximize harvesting. ...
Article
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As a sustainable alternative regarding environmental impact, cost-effectiveness, and social integration, solar energy is expected to become an ever more ubiquitous part of our intricate human world. Dropping prices and growing demand are making it more viable for a variety of solar devices to be implemented in urban and other complex environments. From devices helping people meet their energy needs to solar-powered drones fulfilling urban services like maintenance, security, carrying goods, or even transporting people. These environments involve constrained and dynamic conditions, encouraging the use of solar harvesting devices that can freely adopt tailor-made positioning and tracking strategies to make the most of available resources. A crucial challenge is improving the geometrical flexibility and efficiency of modeling capabilities. In particular, developing practical approaches that account for detailed shadow effects in complex scenarios can be computationally challenging, and it is not clear how different approaches compare face-to-face in urban contexts and with freely defined harvesting surfaces. In this work, four shadow modeling approaches are developed and demonstrated in urban scenes of varying complexity; accuracy and precision are characterized versus computational cost; run-time trends are analyzed as functions of scene complexity, and energy estimation implications are examined. The approaches converge within 1% deviations, and the highest performing approach is three orders of magnitude faster than the most computationally costly. This work supports the selection and development of accurate, efficient, and flexible modeling frameworks that will play a role in enabling a diverse range of solar harvesting devices in challenging urban environments.
... There have been several efforts in developing vehicle controllers for a single UAV tracking targets in urban environments [11], [12], [13], [14], [15], [16], [17]. Zhao et al. [11] developed a vision algorithm based on YOLO to detect a target in an urban environment. ...
... Kim and Crassidis [15] assigned circular paths to maximize the visibility of the targets and decision to change these circular paths was carried out online using an approximate dynamic programming approach. Wu et al. [16] developed an improved whale optimization framework to determine paths for the UAV to maximize the energy obtained by the solar panels, while considering the presence of obstacles and their shadow into account. They formulated the problem without considering the target tracking aspect. ...
... However, due to some inherent drawbacks in both low energy 10 storage and high energy-consuming rate, the UAV may drain its stored energy quickly which limits its service range and affects its service effectiveness. Although the UAV can utilize solar radiation as energy [6], this is not sufficient to meet the UAV's demand for energy since the charging rate of such a solar-powered UAV is usually not high enough. Further, Amazon designates delivery vans for transporting UAVs almost 15 to their destinations and further launching them to fulfill the last-mile delivery services [7]. ...
... Due to constraint (14), all nodes in N = . By checking backward along the path that contains (V 5 , V 6 ), we find the optimal path as V 6 V 5 V 0 , which is shown by the green line in Fig. 1. Proof. ...
Article
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The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for recharging on the way before reaching the destination. To address this issue, existing works focus more on UAV's offline path planning with designated system vehicles providing charging service. Nevertheless, in some emergency cases and rural areas where system vehicles are not available, public vehicles can provide more cost-saving and feasible service in UAV travelling. In this paper, we explore how a single UAV can save flying distance by exploiting public vehicles for the purpose of minimizing the overall travel time of the UAV, which is from the perspective of online algorithm. For the offline setting where the information of future vehicles is known far ahead of time, we present an O(n 2)-time shortest-path-like optimal solution by delicately transforming the problem into a graph capturing both time and energy constraints. For the online setting where public vehicles appear in real-time and only inform the UAV of their trip information some certain time t beforehand, we first construct lower bounds on the competitive ratio for different t. Then, we propose two online algorithms, including a greedy algorithm MYOPICHITCHING that greedily hitches truck rides and an improved ? A preliminary version of this paper appeared in the proceedings of COCOON 2021 [1]. ⇤ algorithm t-ADAPTIVE that further tolerates a waiting time in hitching a ride. Our theoretical analysis shows that t-ADAPTIVE is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as t increases.
... As a large fraction of energy demand comes from cities [5], bringing energy production closer to consumers reduces transmission and distribution losses [6,7] and may make the electrical grid more efficient and adaptable. There has been notable research interest in urban solar harvesting, including solar accessibility in developing cities [4,8], energy implications of shadow distribution on roofs [9], solar potential maps [10] toward more sustainable urban planning [11], energy-efficient architecture [12], solar-powered aerial vehicles [13], fixed building-integrated PV [14][15][16], and dynamic building envelopes [17]. Other complex environments include heliostats [18], step-like fields [19], and windsolar dual land use [20]. ...
... This simplifies energy estimations, especially concerning potentially intricate shadowing effects [6,11], but it may restrain flexibility in environments with challenging constraints [36]. In complex environments, more dynamic tracking [13] and positioning strategies [28] can improve energy efficiency and versatility. For instance, solar modules may be positioned in 3D arrangements [34], in nonregular grids [24,36], or use individualized [35] and context-aware tracking (e.g., backtracking [22]) to minimize shadows and maximize harvesting. ...
... The criterion is based on the agent's fitness value, and the quality of current solution is considered. An adaptive chaos-Gaussian switching solving strategy is introduced in Ref. [123] , and the decision on operation selection (chaos Table 6 Modifications of some new PMH algorithms. ...
... In GA-artificial potential field method [151] , GA is responsible for the global search, and the artificial potential field method searches in local and modifies the individual's fitness value. In WOA, the coordinated decision-making mechanism in GWO algorithm is used to replace the best solution when updating the formula of bubble-net attacking [123] . ...
Article
Meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.
... Recently, several works have been developed to address the multi-objective path planning problem for UAVs, and different path planning algorithms have been proposed. For example, in Wu et al. (2018), Yang et al. (2015), Macharet et al. (2010), Fu et al. (2012), multi-objective path planning methods (MOPP) for UAVs based on the evolutionary algorithms are proposed to find a path avoiding static obstacles in the environment. In Mittal and Deb (2007), authors proposed Content courtesy of Springer Nature, terms of use apply. ...
Article
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This paper presents a multi-objective hybrid path planning method MOHPP for unmanned aerial vehicles (UAVs) in urban dynamic environments. Several works have been proposed to find optimal or near-optimal paths for UAVs. However, most of them did not consider multiple decision criteria and/or dynamic obstacles. In this paper, we propose a multi-objective offline/online path planning method to compute an optimal collision-free path in dynamic urban environment, where two objectives are considered: the safety level and the travel time. First, we construct two models of obstacles; static and dynamic. The static obstacles model is based on Fast Marching Square (FM2) method to deal with the uncertainty of the geography map, and the unexpected dynamic obstacles model is constructed using the perception range and the safety distance of the UAV. Then, we develope a jointly offline and online search mechanism to retrieve the optimal path. The offline search is applied to find an optimal path vis-a-vis the static obstacles, while the online search is applied to quickly avoid unexpected dynamic obstacles. Several experiments have been performed to prove the efficiency of the proposed method. In addition, a Pareto front is extracted to be used as a tool for decision making.
... More commendable, they are not affected by the increased complexity (Gharehchopogh and Gholizadeh 2019). Perhaps in view of these advantages, NIAs based on global optimization techniques have, for example, been applied to find orbital transfer trajectories (Pontani and Conway 2010) or path planning for solar-powered UAV in urban environment (Wu et al. 2018). NIAs can be broadly divided into three categories: evolutionary algorithms (EAs), swarm intelligence (SI) and physics-based (PB) algorithms (Jain et al. 2019). ...
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Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel Harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing Harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem.
... However, due to the nature of the UAV/drone in both the low energy storage and high-rate flying consumption, it quickly drains its stored energy, which limits the delivery range and affects the service effectiveness remarkably. Although the UAV can be solar-powered by utilizing solar radiation as energy [2], this is not sufficient since the charging-rate is not high enough. Fortunately, the UAV is able to dock with road vehicles automatically [17], which makes it possible for UAV to team up with trucks spontaneously and instantaneously for reducing the transportation cost. ...
Preprint
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The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for charging power on the way before reaching the destination. To address this issue, existing works focus more on UAV's path planning with designated system vehicles providing charging service. However, in some emergency cases and rural areas where system vehicles are not available, public trucks can provide more feasible and cost-saving services and hence a silver lining. In this paper, we explore how a single UAV can save flying distance by exploiting public trucks, to minimize the travel time of the UAV. We give the first theoretical work studying online algorithms for the problem, which guarantees a worst-case performance. We first consider the offline problem knowing future truck trip information far ahead of time. By delicately transforming the problem into a graph satisfying both time and power constraints, we present a shortest-path algorithm that outputs the optimal solution of the problem. Then, we proceed to the online setting where trucks appear in real-time and only inform the UAV of their trip information some certain time $\Delta t$ beforehand. As a benchmark, we propose a well-constructed lower bound that an online algorithm could achieve. We propose an online algorithm MyopicHitching that greedily takes truck trips and an improved algorithm $\Delta t$-Adaptive that further tolerates a waiting time in taking a ride. Our theoretical analysis shows that $\Delta t$-Adaptive is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as $\Delta t$ increases.
... Introduced the genetic operators of mutation and crossover into GSO YongBo, C. et al. [16] Modified wolf pack search (WPS) algorithm Introduced the mutation operators into WPS GaiGe, W. et al. [17] Improved bat algorithm (IBA) Combined the BA with Differential Evolution (DE) Wu, J. et al. [18] Improved whale optimization algorithm (IWOA) ...
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The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.
... However, due to the nature of the UAV/drone in both the low energy storage and high-rate fly consumption, it quickly drains its stored energy, which limits the delivery range and affects the service effectiveness remarkably. Although the UAV can be solar-powered by utilizing solar radiation as energy [2], this is not sufficient since the charging-rate is not high enough. Fortunately, the UAV is able to dock with road vehicles automatically [18], which makes it possible for UAV to team up with trucks spontaneously and instantaneously for reducing the transportation cost. ...
Conference Paper
Full-text available
The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for charging power on the way before reaching the destination. To address this issue, existing works focus more on UAV's path planning with designated system vehicles providing charging service. However, in some emergency cases and rural areas where system vehicles are not available, public trucks can provide more feasible and cost-saving services and hence a silver lining. In this paper, we explore how a single UAV can save flying distance by exploiting public trucks, to minimize the travel time of the UAV. We give the first theoretical work studying online algorithms for the problem , which guarantees a worst-case performance. We first consider the offline problem knowing future truck trip information far ahead of time. By delicately transforming the problem into a graph satisfying both time and power constraints, we present a shortest-path algorithm that outputs the optimal solution of the problem. Then, we proceed to the online setting where trucks appear in real-time and only inform the UAV of their trip information some certain time ∆t beforehand. As a benchmark, we propose a well-constructed lower bound that an online algorithm could achieve. We propose an online algorithm Myopic-Hitching that greedily takes truck trips and an improved algorithm Adaptive that further tolerates a waiting time in taking a ride. Our theoretical analysis shows that Adaptive is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as ∆t increases.
... Zhang et al. [18] integrate phase angle encoding and mutation adaptive mechanisms into the basic fruit fly optimization and applies it to plan flight path in complex 3D environments. Wu et al. [19] propose a solar-powered UAV path planning framework for complex urban environments and solve it using an improved whale optimization algorithm that includes an adaptive switching strategy and a coordinated decision mechanism. Vincent et al. [20] propose a genetic algorithm implemented in parallel on a graphics processing unit and apply it to the UAV path planning problem-solving. ...
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The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.
... In recent years, many researches on optimal flight path planning for solar aircraft have been carried out to increase the PV array output power. [15][16][17][18][19][20][21][22] However, these studies mainly focus on the conventional solar aircraft with planar wing and the benefit of energy conversion is limited. Considering the advantages of sun-tracking system in harvesting solar energy, 23,24 the wingtip-connected solar aircrafts have been proposed recently to achieve optimal sun-tracking flight through dynamically wing-morphing and flight attitude planning, which provides a novel method to increase the flight endurance of solar aircraft at low solar elevation angles. ...
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The optimal yawing angle of sun-tracking solar aircraft is tightly related to the solar azimuth angle, which results in a large arc flight path to dynamically track the sun position. However, the limited detection range of payload usually requires solar aircraft to loiter over areas of interest for persistent surveillance missions. The large arc sun-tracking flight may cause the target area on the ground to be outside the maximum coverage area of payload. The present study therefore develops an optimal flight control approach for planning the flight path of sun-tracking solar aircraft within a mission region. The proposed method enables sun-tracking solar aircraft to maintain the optimal yawing angle most of the time during daylight flight, except when the aircraft reverses its direction by turning flight. For a circular region with a mission radius of 50 km, the optimal flight trajectory and controls of an example Λ-shaped sun-tracking solar aircraft are investigated theoretically. Results demonstrate the effectiveness of the proposed approach to optimize the flight path of the sun-tracking aircraft under the given circular region while maximizing the battery input power. Furthermore, the effects of varying the mission radius on energy performance are explored numerically. It has been proved that both net energy and energy balance remain nearly constant as the radius constraint varies, which enables the solar aircraft to achieve perpetual flight at almost the same latitude as the large arc flight. The method and results presented in this paper can provide reference for the persistent operation of sun-tracking solar aircraft within specific mission areas.
... In [96], the overall time of mission is minimized within specified time delays to reach the target position. Path planning for solar-powered UAVs is investigated in [97]. The path planning problem is solved in three steps: (i) obstacles avoidance and shadow tackling are done through restrained interfered dynamic model, (ii) Gaussian switching and coordinated decision strategy have been introduced in traditional whale optimization algorithm (WOA), and (iii) ASHARE model is proposed for solar radiance and energy. ...
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Wireless networks are expected to provide connectivity to an increasing number of users with heterogeneous requirements. Future wireless networks will integrate aerial and terrestrial networks to provide massive (a large number of users) connectivity. Unmanned aerial vehicles (UAV) can be deployed on-demand to provide connectivity in aerial networks. UAV-assisted wireless networks offer a broad range of applications in an overload situation, broadcasting and advertisement, public safety, disaster management, and many more. However, UAV-assisted wireless networks must use their resources efficiently to maintain key performance indicators. The challenges for the successful deployment of UAV-assisted wireless networks include spectrum efficiency, energy consumption, deployment time, backhaul, and cost of deployment. This paper provides a comprehensive survey of resource management in UAV-assisted wireless networks from an optimization viewpoint. We present the classification of UAVs, their benefits, and applications to highlight UAV-assisted wireless networks’ significance. We then provide a detailed discussion on resource management with metrics including placement of UAVs, UAV trajectory, backhaul, path planning, charging, spectrum, and data offloading. Moreover, different constraints, optimization types, and solutions tailored to support UAV-assisted wireless networks are discussed. Finally, we provide future research directions to address the challenges for the successful deployment of UAV-assisted wireless networks.
... Qu et al. [33] proposed a novel hybrid HSGWO-MSOS algorithm which is a combination of the GWO algorithm and the modified symbiotic organisms search (MSOS) algorithm. To ensure the operation of solar UAVs in an urban environment, Wu et al. [34] proposed an improved IGWO algorithm to overcome the local optimal defect. ...
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Aiming at the three-dimensional path planning of unmanned aerial vehicle (UAV) in the complex environment of material delivery in earthquake-stricken areas, this paper proposes an improved adaptive grey wolf optimization algorithm (AGWO) based on the grey wolf optimization algorithm (GWO). There are two main contributions of the proposed method. Firstly, we propose an adaptive convergence factor adjustment strategy and an adaptive weight factor to update the individual’s position. The effectiveness of the improved algorithm is verified by the convergence analysis and the test function simulation experiment. Secondly, the improved algorithm is applied to UAV path planning, the environmental map model is established by integrating digital elevation map and equivalent mountain threat model, and the performance evaluation function is established by fitting the calculated track length. The simulation results show that the improved AGWO is superior to the traditional intelligent algorithm in convergence precision, speed and stability performance, and it is effective for 3D trajectory optimization in complex environment.
... Qu et al. [35] combined reinforcement learning with GWO to solve the UAV path planning problem. Wu et al. [44] proposed an improved WOA to solve the path planning problem of urban solar UAV. Phung et al. [33] proposed an improved discrete PSO to solve the path planning problem of UAV visual surface detection. ...
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Unmanned aerial vehicle (UAV) inspection is an indispensable part of power inspection. In the process of power inspection, the UAV needs to obtain an efficient and feasible path in the complex environment. To solve this problem, a Golden eagle optimizer with double learning strategies (GEO-DLS) is proposed. The double learning strategies consist of personal example learning and mirror reflection learning. The personal example learning can enhance the search ability of the Golden eagle optimizer (GEO) and reduce the possibility of the GEO falling into the local optimum. The mirror reflection learning can improve the optimization accuracy of the GEO and accelerate the convergence speed of the GEO. To verify the optimization performance of the algorithm, the proposed GEO-DLS and several other algorithms were tested under the CEC2013 test suite. At the same time, the proposed GEO-DLS and the GEO were analyzed for population diversity and exploration-exploitation ratio. Finally, the proposed GEO-DLS is applied to the UAV path planning to generate the initial path, and the cubic B-spline curve is used to smooth the path. These experimental results show that the GEO-DLS has a good performance. The code can be publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/98799-golden-eagle-optimizer-with-double-learning-strategies
... First SPUAV was Sunrise I which was created by Robert Boucher and Roland Boucher in 1974 [4,5] then this research trend began to flourish and many remarkable aircraft are designed and manufactured as Solong [6], Helios [7], Photon [8], the Atlantik Solar, Solar Impulse [9] and others. Research in this field opened several applications [10], as investigation to increase the efficiency of SPUAVs as, optimal flight path planning and control [11,12], trajectory control for maximum power accumulation [13], optimizing cruise speed and sizing [14], power optimization [15], weight estimation [16], optimal turning planning to increase flight endurance of solar powered UAVs [18], maximizing net power in circular turns [19]. Also wing morphing technique has a promising potential in this field [20 -22]. ...
Conference Paper
Although the solar panel is thin, its thickness is considerable compared to the airfoil thickness. This paper aims to evaluate the impact of adding the solar panel over UAV airfoils, namely airfoils AG34 and E231. The airfoil aerodynamic characteristics and the pressure distribution over the airfoil surface are used to evaluate the impact. It is found that adding a solar panel slightly affect the aerodynamic behaviour of the airfoil. Lift and moment are slightly affected, while drag changes depending on the airfoil.
... Several authors have analyzed a possibility of using drones in civil (e.g. urban environment [25]), and military (e.g., the battlefield) applications [26]. ...
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Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.
... These obstacles were implemented with Unity GameObjects and placed within a Unity scene (Figure 2). This approach was selected as it is in line with the methods used and described by similar research projects [19,20]. A key requirement of the project was that the environment be dynamic, to facilitate this, moving spherical obstacles were added. ...
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In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.
... Research in this field opened several applications [10], as investigation to increase the efficiency of SPUAVs as, optimal flight path planning and control [11,12], trajectory control for maximum power accumulation [13], optimizing cruise speed and sizing [14], power optimization [15], weight estimation [16], optimal turning planning to increase flight endurance of solar powered UAVs [18], maximizing net power in circular turns [19]. Also wing morphing technique has a promising potential in this field [20][21][22]. ...
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Recently, a great interest in search of alternate means of power for the traditional fuel for aircraft propulsion is raised so as to decrease gas emissions and reduce operating costs. For the small and micro unmanned aerial vehicles or small transportation aircraft, there are many challenges in the direction of constructing an electric or solar powered airplane whose wings may possibly be sheltered with photo voltaic PV solar panels to harvest sun’s energy for propulsion. Greatest remarkably success solar powered aircraft has attracted the attention of researchers other than UAV and small aircraft supporters. Although the solar panel is thin, its thickness is considerable compared to the airfoil thickness. This paper aims to evaluate the impact of adding the solar panel over a low camber airfoil suitable for low-Reynolds number flights, as mini UAVs. Three panel installation configurations are examined to stand on the most suitable configuration, in terms of aerodynamic efficiency. The analysis is based on the airfoil characteristics (lift, drag, and moment) and the pressure distribution over the airfoil surface. A parametric study is conducted to study the effect of the solar panel size, thickness, and position on the aerodynamic performance.
... More commendable, they are not affected by the increased complexity (Gharehchopogh and Gholizadeh 2019). Perhaps in view of these advantages, NIAs based on global optimization techniques have, for example, been applied to find orbital transfer trajectories (Pontani and Conway 2010) or path planning for solar-powered UAV in urban environment (Wu et al. 2018). ...
Preprint
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Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem.
... In recent decades, promising applications of unmanned aerial system (UAS) with a fleet of miniature fixed-wing UAVs (MAV), spring up for substituting human beings in the unclear and unsettled situation, to execute various tasks, such as, environmental disaster relief [1], precision agriculture [2], fighting forest fires [3], and weather forecasting [4]. It is naturally noteworthy that the path following control of MAV is a precondition to accomplish those operational objectives [5][6][7]. However, owing to the size and low-cost attribute, MAVs are affected much more dramatically than traditional UAV in the presence of what may be considered small environmental disturbances [8], even the modelling parameters of MAV is tough to be accurately acquired. ...
Preprint
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In this paper a solution to the path following control problem for miniature fixed wing unmanned aerial vehicle (MAV) in the presence of inaccuracy modelling parameters and environmental disturbances is presented. We introduce a two-layered framework to collaborate guidance level with control level. A modified vector fields based path following methodology is proposed in the kinematics phase to track a Dubins path with straight line segments and circle ones. Then a Proportional-Integral-Derivative (PID) controller based on feedback linearization and gain scheduling techniques is designed such that the MAV can reject nonlinear dynamics, system uncertainties and disturbances by using a robust fuzzy control scheme. Eventually, by giving comparison test with control effort and track error as assessment metrics, both the practicality of the framework and the outperformance of the proposed algorithm are well demonstrated.
... Wu el al. [134] suggested an Improved Whale Optimization Algorithm (IWOA) and Restrained Interfered Fluid Dynamic System (RIFDS) for solving the UAV path planning problem. The performance of IWAO was evaluated in a 3D static urban environment using 5 cubic and 6 cylindrical static obstacles. ...
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Path planning is one of the most important steps in the navigation and control of Unmanned Aerial Vehicles (UAVs). It ensures an optimal and collision-free path between two locations from a starting point (source) to a destination one (target) for autonomous UAVs while meeting requirements related to UAV characteristics and the serving area. In this paper, we present an overview of UAV path planning approaches classified into five main categories including classical methods, heuristics, meta-heuristics, machine learning, and hybrid algorithms. For each category, a critical analysis is given based on targeted objectives, considered constraints, and environments. In the end, we suggest some highlights and future research directions for UAV path planning.
... In the recent decades, promising applications of unmanned aerial systems with a fleet of miniature fixed-wing unmanned aerial vehicles (MAVs) have been developed to substitute human beings in the unclear and unsettled situations, to execute various tasks, such as environmental disaster relief [1], precision agriculture [2], fighting forest fires [3], and weather forecasting [4]. Naturally, the path following control of MAV is a precondition to accomplish those operational objectives [5][6][7]. However, owing to the size and cost-effectiveness, MAVs are more influenced than traditional unmanned aerial vehicles (UAVs) in the presence of minor environmental disturbances [8]; the modeling parameters of MAVs are difficult to accurately acquire. ...
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This study presents a solution to the path following control problem for miniature fixed-wing unmanned aerial vehicles (MAVs) in the presence of inaccuracy modeling parameters and environmental disturbances. We introduce a two-layered framework to combine the guidance level with the control level. A modified vector field-based path following methodology is proposed in the kinematics phase to track a Dubins path with a straight line and circular segments. Subsequently, a proportional integral derivative (PID) controller based on feedback linearization and gain scheduling techniques are designed such that the MAV can reject nonlinear dynamics, system uncertainties, and disturbances using a robust fuzzy control scheme. Eventually, using a comparison test with control effort and track error as assessment metrics, the practicality of the framework and the outperformance of the proposed algorithm are well demonstrated.
... Both centralized and distributed MPC were used to collaborate the multi-UAVs and avoid the occlusion [26,27]. These methods for the occlusion of LOS caused by dense obstacles are also used to solve the charging problem of solar-powered UAVs in urban environment [28,29]. ...
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Unmanned aerial vehicles (UAVs) with limited field of view are utilized to track a moving ground target continuously in urban environment. In urban environment, the sight lines of UAVs to the target are easily blocked by dense obstacles. To overcome this difficulty, the model predictive control (MPC) based collaborative tracking control is proposed with the goal of the maximum visibility of target. First, a visible probability based performance index is proposed, and the flight planning strategy of maximum the phase difference is obtained as a consequence. Then a centralized MPC based collaborative control problem is solved to obtain the optimal control signals. The joint cost function consists of four parts which aims at tracking target, avoiding collision, avoiding the blind area and maintaining the maximum visibility, respectively. The effectiveness of the proposed collaborative strategy is verified by simulation. Compared with the traditional MPC-based collaborative method, the proposed maximum visible probability index provides an optimal dynamic formation structure for multi UAVs to guarantee the tracking of the moving ground target in urban environment.
Chapter
This chapter discusses the use of drones in healthcare with a specific focus on humanitarian logistics. Drones have already been used in healthcare in different aspects, including transfer of blood products, search and rescue missions, or collecting different types of data including aerial photographs, air quality, or radiation levels. Even though the published research evidence in the area of “drones in healthcare” is almost 1% of the broader area of “drones,” the progress in public acceptance, regulations, as well as technology is undeniable. This chapter summarizes the different aspects regarding the use of drones in healthcare, while specifically focusing on humanitarian logistics. The SWOT analysis indicate that the strengths and opportunities weigh more than the weaknesses and threats, suggesting that drones will revolutionize the way medical supplies are delivered within the coming years.
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The conversion efficiency of solar energy and the capacity of energy storage batteries are the key technologies limiting the development of solar-powered aircraft. In this paper, a mission-oriented design for the 3-dimensional (3D) path planning of solar-powered unmanned aerial vehicles (SP-UAVs) using limited solar energy to maximize the mission effectiveness is presented. Based on the solar radiation received model, the energy model, and the kinetic and kinematic model of the SP-UAV, a task planning problem oriented to multiobjective optimization is proposed. Both the pseudospectral and colony algorithms are proposed to search for the optimal mission path, and their joint optimization is employed to realize continuous flight and improve the flight mission capabilities. Explicitly, a multiobjective joint strategy is developed, including maximum power ascending, maximum range flight, maximum glide endurance, and minimum power level flight at night. Numerical and simulation results indicate that our proposed design outperforms the existing approaches not only in solar energy utilization but also in universality.
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Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum. In this paper, a nature-inspired methodology called Interfered Fluid Dynamic System (IFDS) is extended to anti-local-optimum obstacle avoidance in unknown 3D environments for the first time and a novel fluid-based path planning framework is proposed. First, the detection region of the agent is discretized. Then, spherical virtual obstacles (SVOs) located at detected discrete points are generated and memorized. Thus, obstacle avoidance in unknown environments is transformed into the avoidance of known SVOs. Next, the currently generated and memorized SVOs are input to the core of the framework, the IFDS algorithm, to produce repulsive effects, and the corresponding 3D avoidance path is resolved. On this basis, to address local optimum in cases with non-convex obstacles, and considering compatibility with the IFDS, the direction coefficient and sink-heading angular rate adjustment strategies, which belong to the same system as the IFDS, are introduced to modify the IFDS in this framework. Finally, receding horizon control is introduced to improve the obstacle avoidance performance. Simulations show that the proposed framework can enable the agent to autonomously jump out of the 3D non-convex obstacle environments with typical features of the local optimum, including wall-like and cave-like obstacles, and safely reach the destination.
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Aiming at the autonomous collision avoidance problem for unmanned vehicles (UAVs) in low altitude environment, a novel decision mechanism based on 3D dynamic collision region is proposed in this paper. First, the models of collision avoidance are built, which relates to the UAV kinematic characteristics and the environment. Second, the 3D dynamic collision region is established, which contains the safety information of UAV relates to time and distance simultaneously. Besides, for the design of decision mechanism, the information given by the dynamic collision region is fully exploited and several novel definitions are proposed. Third, based on the safety criterions given by the dynamic collision region, a novel decision mechanism for collision avoidance is proposed. By choosing actions such as path re-planning and emergency maneuvers properly, the decision mechanism allows the UAV to better adapt to the complex environment and enhance the flight safety. Finally, simulations are carried out to verify the effectiveness of our proposed framework.
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This paper presents an interval multi-objective path planning (PP) scheme for patrol robot in nuclear power plant. The purpose of this PP scheme is to find collision-free paths with the shortest length and smallest risk degree. Firstly, a novel workspace modeling method is proposed to describe the static PP environment of patrol robot in nuclear power plant. Then considering the conflicts of the shortest length and smallest risk degree, an interval multi-objective particle swarm optimization (IMOPSO) method is used. In the IMOPSO, an ingenious interval update law for the particle’s global best position and local best position based on the crowding distance of each risk degree interval is used to increase the diversity of population, and an iterative procedure is adopted to update the particle’s position when the found paths are collided with some existing obstacles. Finally, three representative simulation tests are used to verify the validity of proposed IMOPSO method. Results show that comparing with other three well-known multi-objective evolutionary algorithms, our proposed method has the advantages of finding a better Pareto optimal paths.
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In the feature selection process, reaching the best subset of features is considered a difficult task. To deal with the complexity associated with this problem, a sophisticated and robust optimization approach is needed. This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The proposed improvement assists the BPSO to avoid local optima obstacles via boosting its exploration ability. In the BPSO-EPD, the worst half of the solutions are discarded by repositioning them around the optimal solutions selected from the best half. Six natural selection mechanisms comprising Best-based, Tournament, Roulette wheel, Stochastic universal sampling, Linear rank, and Random-based are employed to select guiding solutions. To assess the performance of the proposed improvement, 22 well-regarded datasets collected from the UCI repository are employed. The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants. Taking SpecEW dataset as an example, an increment of 6.7% accuracy can be achieved for BSPO-TEPD. Consequently, BPSO-TEPD approach also outperformed other well-known optimizers, including two binary variants of PSO using S-shaped transfer function (SBPSO) and V-shaped transfer function (VBPSO), Binary Grasshopper Optimization Algorithm (BGOA), Binary Gravitational Search Algorithm (BGSA), Binary Ant Lion Optimizer (BALO), Binary Bat algorithm (BBA), Binary Salp Swarm Algorithm (BSSA), Binary Whale Optimization Algorithm (BWOA), and Binary Teaching-Learning Based Optimization (BTLBO). The result emphasizes the excellent behavior of EPD strategies in evolving the ability of BPSO when dealing with feature selection problems.
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In the past decade, Chinese agriculture has been transforming from a small-scale fragmented smallholder economy to large-scale modern agriculture, which has prompted the development of mobile robot systems in agriculture. Multi-robot autonomous navigation has become the most fundamental and daunting challenge in this development process, involving autonomous control, path planning and other aspects. In this paper, the cooperative control strategies and path planning methods for agricultural mobile robots are described, and the application of path planning methods in different agricultural scenarios is analyzed in detail from the perspectives of both online and offline planning. The conclusions show that multi-robot collaborative path planning has been successfully applied to the navigation of agricultural robotic operation scenarios, and the future development should be towards the direction of scale, autonomy, and intelligence.
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This paper proposes a novel Dynamic Adaptive Ant Lion Optimizer (DAALO) for route planning of unmanned aerial vehicle (UAV). Ant Lion Optimizer (ALO) is a new intelligent algorithm motivated by the phenomenon that ant lions hunt ants in nature, showing the great potential to solve the optimization problems of engineering. In the proposed DAALO, the random walk of ants is replaced by Levy Flight to make ALO escape from local optima more easily. Besides, by introducing the improvement rate of population as the feedback, the size of trap is adjusted dynamically based on the 1/5 Principle to improve the performance of ALO including convergence accuracy, convergence speed and stability. Compared to some other bio-inspired methods, the proposed algorithm are utilized to find the optimal route in two different environments such as mountain model and city model. The comparison results demonstrate the effectiveness, robustness and feasibility of DAALO.
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In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional (3-D) path planning problem of unmanned aerial vehicle (UAV) in static environment. Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened. The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced. By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths. And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually. Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path. If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV. In most cases, a flyable path can be obtained eventually. Simulation results demonstrate the effectiveness of this method.
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This paper presents a Co-evolutionary Improved Genetic Algorithm (CIGA) for global path planning of multiple mobile robots, which employs a co-evolution mechanism together with an improved genetic algorithm (GA). This improved GA presents an effective and accurate fitness function, improves genetic operators of conventional genetic algorithms and proposes a new genetic modification operator. Moreover, the improved GA, compared with conventional GAs, is better at avoiding the problem of local optimum and has an accelerated convergence rate. The use of a co-evolution mechanism takes into full account the cooperation between populations, which avoids collision between mobile robots and is conductive for each mobile robot to obtain an optimal or near-optimal collision-free path. Simulations are carried out to demonstrate the efficiency of the improved GA and the effectiveness of CIGA. Crown Copyright (c) 2013 Published by Elsevier B.V. All rights reserved.
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The aim of this work is to evaluate the potential direct and diffuse solar radiation aggregated at a point location in an urban area. With the three-dimensional (3D) SOlar RAdiation Model (SORAM) presented here, the paper makes three key contributions. Firstly, the model augments the Perez et al. (1990) model by accounting for the aggregated contribution of diffuse radiation using ray-tracing methods. Secondly, the model demonstrates the use of a randomly generated city building distribution and terrain map to simulate the 3D urban solar radiation exposure at any time or over a selected time period. Thirdly, we validate our results using empirical sunlight data measured from a real urban area (Sheffield Solar Farm), and also validate our results against the Perez et al. (1990) model under conditions of no shading.
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In this paper, energy optimal surveillance trajectories for unmanned aerial vehicles (UAV) are explored. The main objective is to have maximum sensor coverage range while maintaining a perpetual flight in the presence of uncertainties. A solar-powered UAV is equipped with photovoltaic cells mounted on its wings and rechargeable batteries. The photovoltaic cells generate solar energy based on the position of the sun, attitude of the UAV, and sky clarity. The vehicle aims to optimize the energy storage in the batteries and coverage during the day while the availability of solar radiation is uncertain and the sensor resolution diminishes because of altitude gain. A model for optimal coverage, path planning, and power allocation in a solar-powered UAV is proposed and the corresponding simulation results are presented. In addition, the effect of maximum altitude gain on the energy storage is studied based on a reduced hybrid model. An online setting is proposed to represent the solar radiation uncertainties. This approach demonstrates convergence to the best fixed strategy in both theory and simulation results.
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This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html.
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Three-dimension path planning of uninhabited combat aerial vehicle (UCAV) is a complicated optimal problem, which mainly focused on optimizing the flight route considering the different types of constrains under complex combating environment. A novel predator-prey pigeon-inspired optimization (PPPIO) is proposed to solve the UCAV three-dimension path planning problem in dynamic environment. Pigeon-inspired optimization (PIO) is a new bio-inspired optimization algorithm. In this algorithm, map and compass operator model andlandmark operator model are used to search the best result of a function. The prey-predator concept is adopted to improve global best properties and enhance the convergence speed. The characteristics of the optimal path are presented in the form of a cost function. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO, particle swarm optimization (PSO) and different evolution (DE)in solving UCAV three-dimensional path planning problems.
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Cityscapes provide a complex environment, where solar radiation is unevenly distributed, especially since urban features started to propagate more and more vertically. Due to the dynamic overshadowing effects present on building surfaces, quantifying these phenomena is essential for predicting reductions in solar radiation availability that can significantly affect potential for solar energy use. Numerical radiation algorithms coupled with GIS tools are a pathway to evaluate those complex effects. Accurate representation of the terrain, vegetation canopy and building structures allows better estimation of shadow patterns. Higher spatial and temporal resolutions deliver more detailed results, but models must compromise between accuracy and computation time. In this paper, models ranging from simple 2D visualization and solar constant methods, to more sophisticated 3D representation and analysis, are reviewed. Web-based solar maps, which rely on the previous features to successfully communicate the benefits of the solar resource to the public and support in the policy-making process, are also addressed.
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Solar energy is considered to be the most reliable source in the future, and applying solar energy for flight is one of the most promising utilizations of renewables. Since the invention of solar-powered aircraft in 1974, the methods to extract and store energy for it have become the important research points all around the world. Currently, the main methods to do so are photovoltaic cell, rechargeable battery, and component of maximum power point tracking (MPPT). These years, many institutions and scholars have dedicated great efforts to the relative research. Besides, there are also several other methods in consideration, such as to extract energy from wind shear and store energy by gravitational potential. However, it is still not a simple task for the designers of solar-powered aircraft to select a particular technique from a number of existing techniques, since each technique has certain advantages and disadvantages with respect to different performance indices to fulfill the special requirements. Thus, the aim of this paper is to review in detail the working principle of different methods to extract and store energy, and to compare their performances on the basis of desirable features applied on solar-powered aircraft, and to provide some guidance principles for designers to select proper methods.
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This paper is concerned with the problem of delay and mode-dependent robust nonrational dynamic output feedback controller (DOFC) synthesis for a class of continuous-time semi-Markovian jump linear systems (S-MJLSs) with time-varying delay. Due to the relaxed conditions on the stochastic process, the S-MJLSs are with time-varying transition rates and can describe a broader class of dynamical systems than the traditional Markovian jump linear systems. By introducing a two-term approximation for the time-varying delay, the original system is firstly reformulated into a feedback interconnection configuration, which is well-posed in the sense that the scaled small gain (SSG) technique can be applied to the reformulated system to derive robust performance analysis criteria. Then, based on a semi-Markovian Lyapunov–Krasovskii formulation of SSG condition combined with the sojourn-time fractionizing technique, the performance analysis and mode-dependent nonrational DOFC synthesis conditions for the underlying S-MJLSs are developed, respectively. It is shown that the controller gains can be obtained in terms of linear matrix inequalities. Finally, simulation studies are provided to illustrate the effectiveness and superiority of the proposed design method.
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This paper considers the optimization of flight trajectories for solar-powered aircraft. This work is unique relative to past work because flight path is constrained to repeatedly traverse a specified closed ground path. Constraints of this form are of interest in a variety of missions where the goal is to loiter near a fixed point on the ground. The performance index to be maximized is the average input power to the battery over each cycle of the ground path. It is advantageous to allow the periodic flight path to have altitude variations because during both ascent and descent there are opportunities to increase the angle of sun exposure to the aircraft solar array. A novel procedure for solving the related optimization problem is described that addresses the implementation of a difficult state constraint: the flight path must belong to the surface of the vertical cylinder whose base is the closed ground path. Results for a wide collection of optimization examples are described, which lead to an important conclusion. By allowing aircraft speed and altitude to vary iris possible to obtain an average input battery power greater than the optimal power for constant speed and constant altitude.
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This paper investigates the problem of quantized filtering for a class of continuous-time Markovian jump linear systems with deficient mode information. The measurement output of the plant is quantized by a mode-dependent logarithmic quantizer, and the deficient mode information in the Markov stochastic process simultaneously considers the exactly known, partially unknown, and uncertain transition rates. By fully exploiting the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition for quantized performance analysis is first derived, and then two approaches, namely, the convex linearization approach and iterative approach, to the filter synthesis are developed. It is shown that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities (LMIs) or bilinear matrix inequalities (BMIs). Finally, two illustrative examples are given to show the effectiveness and less conservatism of the proposed design methods.
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Solar-powered airplanes are studied in this research. A solar-powered airplane consumes solar energy instead of traditional fossil fuels; thus it has received a significant amount of interest from researchers and the public alike. The historical development of solar-powered airplanes is reviewed. Notable prototypes, particularly those sponsored by the government, are introduced in detail. Possible future applications of solar-powered airplanes in the civilian and military fields are proposed. Finally, the challenges being faced by solar-powered airplanes are discussed. This study proposes that the solar-powered airplanes are potential alternatives to some present technologies and that they complement current satellites, traditional airplanes, airships, and balloons. However, these planes require further development and enormous technical obstacles must be addressed.
Conference Paper
In this paper, the path planning strategy for solar powered UAVs at low altitude is examined including the weather factor in energy harvesting. The designed path will maximize the difference between the collected energy and the consumed energy, named net gain of energy, when flying from the specified initial point to the final point. A weather map providing information such as regional precipitation is utilized to predict the solar spectral density for the concerned areas. We propose a graph based approach which divides the concerned areas into small grids to evaluate the solar spectral density corresponding to the local weather and then build the energy intensity distribution map as a function of coordinates. The Bellman-Ford algorithm is utilized to find the optimal path which yields maximum net gain of energy at terminal point. Simulation results for level flight are presented.
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Path planning of Uninhabited Aerial Vehicle (UAV) is a complicated global optimum problem. In the paper, an improved Gravitational Search Algorithm (GSA) was proposed to solve the path planning problem. Gravitational Search Algorithm (GSA) is a newly presented under the inspiration of the Newtonian gravity, and it is easy to fall local best. On the basis of introducing the idea of memory and social information of Particle Swarm Optimization (PSO), a novel moving strategy in the searching space was designed, which can improve the quality of the optimal solution. Subsequently, a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search. Particle position was updated according to the selection rules of survival of the fittest. In this way, the population is always moving in the direction of the optimal solution. The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO, basic GSA and two other GSA models.
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Development of solar-powered High-Altitude Long-Endurance (HALE) aircraft has a great impact on both military and civil aviation industries since its features in high-altitude and energy source can be considered inexhaustible. Owing to the development constraints of rechargeable batteries, the solar-powered HALE aircraft must take amount of rechargeable batteries to fulfill the energy requirement in night, which greatly limits the operation altitude of aircraft. In order to solve this problem, a new Energy Management Strategy (EMS) is proposed based on the idea that the solar energy can be partly stored in gravitational potential in daytime. The flight path of HALE aircraft is divided into three stages. During the stage 1, the solar energy is stored in both lithium–sulfur battery and gravitational potential. The gravitational potential is released in stage 2 by gravitational gliding and the required power in stage 3 is supplied by lithium–sulfur battery. Correspondingly, the EMS is designed for each stage. The simulation results show that the aircraft can always keep the altitude above 16 km with the proposed EMS, and the power consumed during night can be also alleviated. Comparing with the current EMS, about 23.5% energy is remained in batteries with the proposed EMS during one day–night cycle. The sensitivities of the improvement of crucial technologies to the performance of aircraft are also analyzed. The results show that the enhancement of control and structural system, lithium–sulfur battery, and solar cell are ranked in descending order for the performance improvement of solar-powered HALE aircraft.
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This paper considers energy-optimal path planning and perpetual endurance for unmanned aerial vehicles equipped with solar cells on the wings, which collect energy used to drive a propeller. Perpetual endurance is the ability to collect more energy than is lost during a day. This paper considers two unmanned aerial vehicle missions: 1) to travel between given positions within an allowed duration while maximizing the final value of energy and 2) to loiter perpetually from a given position, which requires perpetual endurance. For the first mission, the subsequent problem of energy-optimal path planning features the coupling of the aircraft kinematics and energetics models through the bank angle. The problem is then formulated as an optimal control problem, with the bank angle and speed as inputs. Necessary conditions for optimality are formulated and used to study the optimal paths. The power ratio, a nondimensional number, is shown to predict the qualitative features of the optimal paths. This ratio also quantifies a design requirement for the second mission. Specifically, perpetual endurance is possible if and only if the power ratio exceeds a certain threshold. Comparisons are made of this threshold between Earth and Mars. Implications of the power ratio for unmanned aerial vehicle design are also discussed. Several illustrations are given.
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Path planning of Uninhabited Combat Air Vehicle (UCAV) is a rather complicated global optimum problem which is about seeking a superior flight route considering the different kinds of constrains under complex combat field environment. Artificial Bee Colony (ABC) algorithm is a new optimization method motivated by the intelligent behavior of honey bees. In this paper, we propose an improved ABC optimization algorithm based on chaos theory for solving the UCAV path planning in various combat field environments, and the implementation procedure of our proposed chaotic ABC approach is also described in detail. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method.
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Contenido: Comparación de algoritmos evolutivos (evolución orgánica y resolución de problemas; algoritmos evolutivos específicos; panoramas artificiales; comparación empírica); extensión de algoritmos genéticos (selección; mutación; experimento en meta-evolución); datos para la función Fletcher-Powell; datos provenientes de experimentos de selección; el ambiente multiprocesador; símbolos matemáticos.