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Swarm encountering obstacle (a) the initial configuration, (b) shortest path swarm distribution, (c) swarm distribution utilizing the proposed approach.
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The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided i...
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... a swarm of autonomous drones encounters an obstacle(s), the agents take local decisions to perform collision avoidance maneuvers. Fig. 3 shows an example scenario of a swarm with eight agents avoiding an obstacle using the two different approaches. The initial configuration is illustrated by agents in "blue" (Fig. 3(a)). The cases illustrated are as follows: 1) swarm in distribution while performing collision avoidance using shortest path approach (Fig. 3(b)), 2) the ...
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... a swarm of autonomous drones encounters an obstacle(s), the agents take local decisions to perform collision avoidance maneuvers. Fig. 3 shows an example scenario of a swarm with eight agents avoiding an obstacle using the two different approaches. The initial configuration is illustrated by agents in "blue" (Fig. 3(a)). The cases illustrated are as follows: 1) swarm in distribution while performing collision avoidance using shortest path approach (Fig. 3(b)), 2) the distribution of the swarm agents with the proposed approach (Fig. 3(c)). The apparent answer to the collision avoidance problem is for each drone to select the nearest end of the ...
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... avoidance maneuvers. Fig. 3 shows an example scenario of a swarm with eight agents avoiding an obstacle using the two different approaches. The initial configuration is illustrated by agents in "blue" (Fig. 3(a)). The cases illustrated are as follows: 1) swarm in distribution while performing collision avoidance using shortest path approach (Fig. 3(b)), 2) the distribution of the swarm agents with the proposed approach (Fig. 3(c)). The apparent answer to the collision avoidance problem is for each drone to select the nearest end of the obstacle and go round the corner as the optimum route, namely: the shortest path approach [38]. As exemplified in the aforementioned figure, the ...
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... agents avoiding an obstacle using the two different approaches. The initial configuration is illustrated by agents in "blue" (Fig. 3(a)). The cases illustrated are as follows: 1) swarm in distribution while performing collision avoidance using shortest path approach (Fig. 3(b)), 2) the distribution of the swarm agents with the proposed approach (Fig. 3(c)). The apparent answer to the collision avoidance problem is for each drone to select the nearest end of the obstacle and go round the corner as the optimum route, namely: the shortest path approach [38]. As exemplified in the aforementioned figure, the optimal formation disturbance for the swarm may not follow the shortest path rule, ...
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... The apparent answer to the collision avoidance problem is for each drone to select the nearest end of the obstacle and go round the corner as the optimum route, namely: the shortest path approach [38]. As exemplified in the aforementioned figure, the optimal formation disturbance for the swarm may not follow the shortest path rule, for example in Fig. 3(b) if each agent moves towards the edge of the obstacle with respect to its own coordinates to follow the shortest path, it will take more time for the swarm to bypass the obstacle since the agents will have to slow down to avoid congestion from neighboring agents. On the other hand, if the agents follow the proposed optimal morphing ...
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... for the swarm as a whole, it may not be. This is due to the fact that delays occur when the swarm has to deviate from its original trajectory to either avoid an obstacle or go through the available gap between the obstacles and the agents have to slow down, wait, or allow for other neighboring agents to go ahead or merge in the queue as shown in Fig. 3(b). Now it is important to note here that if an obstacle, assuming the obstacle is in detection range and both corners are visible, clearly extends towards one side of the swarm does not mean that going for the shortest path will provide optimal results, i.e., the minimum time for the last agent to pass through. Here we are calculating ...
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... the number of population factor and í µí±í µí±í µí± í µí± is the number of obstacles. Then based on the population factor set and the penalty of time, i.e., the group configuration that requires the minimum amount of time to pass the obstacle, the agents are divided into different groups ({groupSet}) is calculated (Line 3), as illustrated in Fig. 3. The agents' distribution into the {groupSet} is done based on the following ...
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... order to estimate the energy-saving effect of the proposed approach, we first consider the energy consumption of the swarm of eight drones while bypassing a single obstacle, as discussed in section 2 and depicted in Fig. 3. In the following discussion, we use the results of [47], where total power required by a drone weighing 20 Newton, having four blades with a rotor radius of 40 cm is plotted against the drone's flying speed. We have selected the nominal speed as 10 m/s, which is a close approximation of the Maximum Endurance speed calculated by [47]. ...
Citations
... Due to their ability to work in a collaborative and cooperative manner, swarms of drones are typically used for surveillance purposes, i.e., tracking and localizing objects, and this is reflected in another survey [4]. One of the most significant challenges regarding the navigation of a swarm of agents is collision avoidance [5]. Collision avoidance systems are responsible for guiding an autonomous agent to safely and reliably avoid potential collisions with other agents in the swarm, as well as with other objects in the environment. ...
... Obstacle Direction [5,15] Figure 11. Randomly generated collision scenario. ...
The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°.
... Autonomous navigation objective is to transport objects without requiring pilots, remote commands, or special infrastructure that allows technical operators to guide the trajectory to complete it [4]. For autonomous navigation success, the main task is the constant and precise determination of the mobile object's position and orientation [5]. ...
... Also, another CF contribution to the IKZ methodology is the correction of the drift error and the estimation of initial conditions in irregular surfaces, avoiding the mismatch between the actual angle and the one calculated in the rotation matrix (equation (2)). A projection error of the acceleration forces is significantly reduced; this avoids erroneous forces distribution in equation (5). Subsequently, the inertial acceleration values are calculated in reference of b-frame a using equation (5). ...
... A projection error of the acceleration forces is significantly reduced; this avoids erroneous forces distribution in equation (5). Subsequently, the inertial acceleration values are calculated in reference of b-frame a using equation (5). Then, it is changed into a navigation frame using equation (6), followed by the numerical integration to calculate velocity v and the ZUPT to determine the "stance" moments. ...
Nowadays, there are different methods used in the autonomous navigation task; current solutions include inertial navigation systems (INS). However, these systems present drift errors that are attenuated by the integration of absolute reference systems such as GPS, and antennas, among others. Consequently, few works concentrate efforts on developing a methodology to reduce drift errors in INS due to the widespread practice of incorporating absolute references into their systems. However, absolute references must be placed beforehand, which is not always possible. This work presents an improvement on our methodological proposal IKZ for tracking and localization of moving objects by integrating a complementary filter (CF). The main contribution of this paper is the methodological proposal in the integration between IKZ and CF, maintaining the restrictive properties to the drift error and significantly improving the handling characteristics of the system in real applications. Furthermore, the IKZ/CF was tested with raw data from an MPU-9255 in order to analyze the results between tests.
... Each robot in the swarm executes relatively simple control routines to accomplish its task. It uses its onboard sensors for awareness of the surrounding environment, whereas it relies on wireless messaging for coordination with other robots of its formation, akin to behavior of individual agents in a multi-agent system [3]. In the following text, the terms agent and robot are used interchangeably. ...
... Of particular interest is the problem of navigation of a swarm of autonomous robots in an unknown environment, such as a GPS-denied area or where no prior map information exists. Work in this field presents a wide array of research challenges, such as the ability to maintain formation, self-localization, collision avoidance, and path finding [3]. Simultaneous localization and mapping (SLAM) is a classical and fundamental technique for mapping and localizing in the field of autonomous robots in environments with no prior map information. ...
Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.
... They integrate a virtual generation tool to operate the environment's data and display the objects of interest. Moreover, [70], and [71], used the SwarmLAb co-simulator to evaluate their swarm formation morphing and energy-efficient formation morphing algorithms, respectively, for the swarm of UAVs. ...
During the last decade, Unmanned Aerial Vehicles (UAVs) gained wide attention and are integrated into diverse systems and deployed into many contexts. The need to assess the UAVs’ performances, impacts of applications, routing protocols, mobility as well as other features in a network is necessary. However, conducting real experiments within UAV-based systems and particularly with multi-UAV-based systems can be extremely costly and complex. To fill this gap simulators, emulators, and frameworks are used to evaluate the performance and viability of UAV-based systems with lower cost and in a short period. Accordingly, researchers and software engineers developed several simulators and co-simulators dedicated to the performance evaluations of UAV-based systems. In this regard, this investigation highlights and identifies the most suitable simulators, co-simulators, emulators, and frameworks for UAV flights and management. Both the tools dedicated to mono-UAV-based systems and the tools dedicated to multi-UAV-based systems are pinpointed. In this paper, the goals, the requirements, the strengths, and the limitations of each studied tool are detailed. Besides that, a UAV simulators catalog is provided. It includes guidelines that will be useful for researchers and help them to identify and to select the adequate tools for UAVs performance analysis that meet their needs.
... That is due to the ability of the agents within the swarm to self localize, self-organize, communicate with other agents, as well as the flexibility and scalibility of the overall swarm making the utilization of swarm of robots ideal for such unknown environments [4]. Similarly, for a swarm to navigate autonomously, in any environment introduces several research challenges, such as keeping or maintaining the formation, collision avoidance, localizing, inter-agent communication, path finding [5]. Among other approaches for localizing, the agents in the swarm can utilize simultaneous localization and mapping (SLAM) to autonomously self localize and navigate in unknown environments with no prior map information [6]. ...
The focus of this work is to present a novel methodology utilizing the classical SLAM technique and integrating with the swarm agents for localizing, guiding, and retrieving the agents towards the optimal path while using only necessary tracker-based information between the agents. While navigating in an unknown environment with no-prior map information, upon encountering large obstacles (out of the field of view detection range of the onboard sensors, the swarm is divided into sub-swarms. This is done while dropping tracking points at every turn. Similarly, the time stamps for every turn taken and the gap width available between obstacles are recorded. Once an agent from any sub-swarm category reaches the destination, the agent broadcasts these tracker points to the rest of the swarm agents. Utilizing this broadcasted key information, the rest of the agents are able to navigate toward the destination without having to find the path. With the help of simulation examples, it is shown that the proposed technique is efficient over other similar randomized turn-based techniques.
... Currently, the UAV swarm scheduling method is mainly used for military reconnaissance scenarios, emphasizing UAV obstacle avoidance, target identification, and strikes, among others. For example, Jawad et al. proposed a method to reduce unnecessary power consumption of sensors and improve the overall energy consumption, and a congestion control method based on a thin-plate splines technique for maintaining formation and avoiding collisions of UAV swarms, respectively [32,33]. The core of the problem in remote sensing observation for emergency scenarios is the efficient execution of predefined data acquisition tasks, which are based on reciprocal full-coverage operations. ...
... We reviewed a lot of literature and did not find a suitable UAV swarm scheduling method that could meet the remote sensing observation requirements of emergency scenarios, so we proposed the UAV swarm scheduling method that is applicable to real-life remote sensing observation of emergency scenarios. Similarly to the objective of reference [33], is the aim is to improve the efficiency of UAV swarms. ...
Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency.
... Formation maintenance algorithms can be outlined into the following three classes [34,35]: (1) leader-follower based approaches, in which all of the agents in the swarm follow the leader and autonomously maintain their respective positions, w.r.t. their neighbours and the leader [36][37][38][39]; (2) virtual structure based approaches, in which all of the agents of the swarm as a whole are considered to be a single compound agent to be navigated along a given trajectory [40][41][42][43]; and, (3) behavior based approaches, in which the agents select their behavior in each situation based on a pre-determined procedure or strategy [44,45]. ...
The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents.
Collision avoidance is one of the most important topics in the robotics field. In this problem, the goal is to move the robots from initial locations to target locations such that they follow the shortest non-colliding paths in the shortest time and with the least amount of energy. Robot navigation among pedestrians is an example application of this problem which is the focus of this paper. This paper presents a distributed and real-time algorithm for solving collision avoidance problems in dense and complex 2D and 3D environments. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with ORCA one of the most important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA while is also more reliable. The proposed method is shown to enable fully autonomous navigation of a swarm of Crazyflies.
