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Tangent A* Planner: Enabling UAV Navigation in Obstacle-Rich Environments
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In this paper, an improved grey wolf optimization algorithm is proposed for the research of UAV path planning in a complex 3D environment. Firstly, a new nonlinear convergence factor is proposed to balance the performance of global search and local development. Secondly, a cubic chaotic mapping is adopted to initialize the wolf population, diversifying the population while improving the uniformity of the population distribution. Finally, a mutation operation is introduced to mutate the individual gray wolf, which enhances the ability of the algorithm to jump out of the local optimum. Three-dimensional environment model is established by elevation data. The simulation results show that the optimal fitness of the improved algorithm is improved by 2.34% compared with that before the improvement, which proves the effectiveness of the algorithm in this paper.
To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.
For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.
Optimal path planning on non-convex maps is challenging: sampling-based algorithms (such as RRT) do not provide optimal solution in finite time; approaches based on visibility graphs are computationally expensive, while reduced visibility graphs (e.g., tangent graph) fail on such maps. We leverage a well-established, and surprisingly less utilized in path planning, geometrical property of convex decompositions i.e. a concave shape can be decomposed into multiple convex shapes. We propose a novel local tangent based approach, inspired by such convex decompositions, to path planning in non-convex maps. Although our local tangent approach is inspired by geometric convex decompositions, it does not require complex decomposition process. Our second contribution is an efficient corner detection method which reasons on binary pixel occupancy maps. Combined with our novel local tangent approach, which intelligently selects nodes from these corners, we modify the standard A* algorithm by feeding these nodes to its open list. With our local tangent approach, only small number of selected corners are fed to A* open list which keeps its size small even for larger maps, resulting in lower convergence time. We formally prove the optimality of our solution. Simulation on our own maps and public dataset (MAPF http://mapf.info/) as well as real-world experiments show that our proposed LTA* algorithm gives better convergence time and shorter path length in environments with both convex and concave obstacles.
For large-scale Unmanned Aerial Vehicle (UAV), it is a challenging problem to automatically generate an optimal path considering the multi constraint. In order to improve the efficiency and optimality of conventional A* algorithm, Bi-directional adaptive A* algorithm is proposed in this paper. Regarding to the defect of expansion efficiency for conventional A* algorithm based on multi direction, directional search strategy is adopted to improve the efficiency of expansion process and guarantee the smooth of the path. Adaptive step strategy and adaptive weight strategy are used to improve the exploration speed and the accuracy of the proposed algorithm. By adopting bi-directional search strategy, the exploration ability of the algorithm can be improved in some complex environment. Finally, rewiring process is introduced to optimize the path length. The simulation for UAV path planning under the multi constrained conditions is carried out and the simulation results show that Bi-directional adaptive A* algorithm has the superiority in run time and path quality.
Modern public services are nowadays capable of conducting complex operations in case of various natural or man-made hazards. In last decades, the responsibilities of the fire services have been significantly extended from ‘ordinary’ fire-fighting to complex operations including technical rescue, but also chemical, biological, radiological, nuclear rescue. Therefore this crucial public service is being constantly equipped with the newest and the most efficient solutions aiming at optimization of their primary activity, which saves victims lives. Thanks to INSARAG guidelines, activities of certified heavy urban search and rescue groups (HUSAR) are based on solid foundations: knowledge and experience gained during historical events in last decades. However, rapid technological progress might be beneficial. The article describes the MOBNET system, that is currently under development. There was a research work conducted to gather end-user requirements in purpose to create a tailor-made solution supporting traditional activities of USAR teams. The target group of the system has been extended, as other public services might benefit from the system implementation as well. The system will use combined DCT – EGNSS technology to track victims.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
A new motion planning method for robots in static workspaces is
presented. This method proceeds in two phases: a learning phase and a
query phase. In the learning phase, a probabilistic roadmap is
constructed and stored as a graph whose nodes correspond to
collision-free configurations and whose edges correspond to feasible
paths between these configurations. These paths are computed using a
simple and fast local planner. In the query phase, any given start and
goal configurations of the robot are connected to two nodes of the
roadmap; the roadmap is then searched for a path joining these two
nodes. The method is general and easy to implement. It can be applied to
virtually any type of holonomic robot. It requires selecting certain
parameters (e.g., the duration of the learning phase) whose values
depend on the scene, that is the robot and its workspace. But these
values turn out to be relatively easy to choose, Increased efficiency
can also be achieved by tailoring some components of the method (e.g.,
the local planner) to the considered robots. In this paper the method is
applied to planar articulated robots with many degrees of freedom.
Experimental results show that path planning can be done in a fraction
of a second on a contemporary workstation (≈150 MIPS), after learning
for relatively short periods of time (a few dozen seconds)
The recent pandemic of COVID-19 has proven to be a test case for Unmanned Aerial Vehicles (UAVs). UAVs have shown great potential for plenty of applications in the face of this pandemic, but their scope of applications becomes limited due to the dependency on ground pilots. Irrespective of the application, it is imperative to have an autonomous path planning to utilize UAVs to their full potential. Collision-free trajectories are expected from the path planning process to ensure the safety of UAVs and humans on the ground. This work proposes a path planning technique where collision avoidance is mathematically proven under an uncertainty prerequisite, that the UAV follows its requested moving position within some threshold distance. This scheme ensures UAV safety by considering the underlying control's system overshoots. Obstacles play a guiding role in selecting collision-free trajectories. These obstacles are modeled as rectangular shapes with interest points defined around their corners. These points further define collision-free permissible edges, and later we apply the Dijkstra algorithm to these edges before having the desired trajectory. Regardless of the size of deployment area, our proposed scheme incurs low computational load due to the dependency on pre-defined interest points only thereby making it suitable for real-time path planning. Simulation results obtained using MATLAB's UAV Toolbox show that the proposed method succeeds in getting short collision-free trajectories.
Unmanned aerial vehicles (UAVs) have emerged as promising platforms for fast, energy-efficient, and cost-effective package delivery. Path planning in 3-D urban environments is critical to drone delivery. The paper proposes a novel tangent-based (3D-TG) method for UAV path planning in 3-D urban environments. When a drone encounters an obstacle, a tangent graph is constructed to generate three sub-paths from both sides and above to bypass an obstacle, one of which is selected according to sophistically designed heuristic rules. The selected sub-path would be constantly adjusted its direction via tangent graph to avoid obstacles until the path can extend to the goal without obstacle collision. To avoid moving obstacles, velocity obstacle is incorporated in the 3D-TG. The experimental results on synthetic and realistic scenarios illustrate that 3D-TG performs well under static, unknown and dynamic environments. More significantly, 3D-TG can also generate a collision-free path for a drone to navigate through simple mazes efficiently, within a reasonable time.
Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this article proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tangent graph method is used to generate two sub-paths, one of which is selected based on heuristic rules when confronting an obstacle. The UAV flies along the selected sub-path and repeatedly adjusts its flight path to avoid obstacles through this way until the collision-free path extends to the target. Considering the UAV kinematic constraints, the cubic B-spline curve is employed to smooth the waypoints for obtaining a feasible path. Compared with A*, PRM, RRT and VFH, the experimental results show that APPATT can generate the shortest collision-free path within 0.05 seconds for each instance under static environments. Moreover, compared with VFH and RRTRW, APPATT can generate satisfactory collision-free paths under uncertain environments in a nearly real-time manner. It is worth noting that APPATT has the capability of escaping from simple traps within a reasonable time.
This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
This paper proposes a bidirectional spline-RRT∗ path planning algorithm for fixed-wing unmanned aerial vehicles (UAVs). The proposed algorithm combines the basic RRT∗ algorithm with the spline method to produce smooth paths, which are essential for fixed-wing UAVs. The proposed bidirectional spline-RRT∗ algorithm generates paths satisfying approach direction constraints on both start and goal points, which makes it significantly different from typical path planning algorithms. Further, the algorithm is asymptotically optimal as the cost of the path found decreases as the number of vertices increases, with the path found finally converging to the optimal path. The proposed algorithm also considers the aerodynamic characteristics of the target aircraft. As a result, the paths produced are within load factor and thrust-to-weight ratio limits, are geometrically and dynamically feasible, sub-optimal, and satisfy approach direction constraints. The results of several simulations conducted confirm these properties of the bidirectional spline-RRT∗ algorithm. In particular, the results of a nonlinear six degree-of-freedom simulation verify the aerodynamic characteristics for the target aircraft, and show that the proposed bidirectional spline-RRT∗ algorithm can be effectively utilized in path planning problems for fixed-wing UAVs.
We present a two step path planning algorithm for unmanned aerial vehicles (UAVs) with kinematic constraints in the presence of polygonal obstacles. We use a visibility graph representation for the environment and a Dubins vehicle approximation to model UAV kinematics. A modified Dijkstra's shortest path algorithm is developed as first step of our approach to plan paths for a UAV. The algorithm in step one takes polynomial time in the number of obstacle vertices in the visibility graph. The second step performs a reverse search on the graph to find feasible paths and uses results of the first step as priors to speed up the search. We present simulation results to substantiate the claims.
In this paper, we present a parallel hybrid metaheuristic that combines the strengths of the particle swarm optimization (PSO) and the genetic algorithm (GA) to produce an improved path-planner algorithm for fixed wing unmanned aerial vehicles (UAVs). The proposed solution uses a multi-objective cost function we developed and generates in real-time feasible and quasi-optimal trajectories in complex 3D environments. Our parallel hybrid algorithm simulates multiple GA populations and PSO swarms in parallel while allowing migration of solutions. This collaboration between the GA and the PSO leads to an algorithm that exhibits the strengths of both optimization methods and produces superior solutions. Moreover, by using the "single-program, multiple-data" parallel programming paradigm, we maximize the use of today's multicore CPU and significantly reduce the execution time of the parallel program compared to a sequential implementation. We observed a quasi-linear speedup of 10.7 times faster on a 12-core shared memory system resulting in an execution time of 5 s which allows in-flight planning. Finally, we show with statistical significance that our parallel hybrid algorithm produces superior trajectories to the parallel GA or the parallel PSO we previously developed.
A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.
This article proposes a tangent graph for path planning of mobile robots among obstacles with a general boundary. The tangent graph is defined on the basis of the locally shortest path. It has the same data structure as the visibility graph, but its nodes represent common tangent points on obstacle boundaries, and its edges correspond to collision-free common tangents between the boundaries and convex boundary segments between the tangent points. The tangent graph requires O(K[sup 2]) memory, where K denotes the total number of convex segments of the obstacle boundaries. The tangent graph includes all locally shortest paths and is capable of coping with path planning not only among polygonal obstacles but also among curved obstacles.
We consider a graph with n vertices, all pairs of which are connected by an edge; each edge is of given positive length. The following two basic problems are solved. Problem 1: construct the tree of minimal total length between the n vertices. (A tree is a graph with one and only one path between any two vertices.) Problem 2: find the path of minimal total length between two given vertices.
A genetic algorithm is a form of evolution that occurs on a computer. Genetic algorithms are a search method that can be used for both solving problems and modeling evolutionary systems. With various mapping techniques and an appropriate measure of fitness, a genetic algorithm can be tailored to evolve a solution for many types of problems, including optimization of a function of determination of the proper order of a sequence. Mathematical analysis has begun to explain how genetic algorithms work and how best to use them. Recently, genetic algorithms have been used to model several natural evolutionary systems, including immune systems.
eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described, 1
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
We introduce the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the beneficial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonholonomic constraints (including dynamics) and high degrees of freedom. An RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected points, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. Several desirable properties and a basic implementation of RRTs are discussed. To date, we have successfully applied RRTs to holonomic, nonholonomic, and kinodynamic planning problems of up to twelve degrees of freedom.
Deep Reinforcement Learning: An Overview
- A Bessede
- M Gargaro
- M T Pallotta
- D Matino
- G Servillo
- C Brunacci
Deep Reinforcement Learning: An Overview
- Bessede
Artificial neural networks: A practical course
- Da Silva
Differential Evolution
- Price