Design and Implementation of Distributed Path Planning Algorithm for a Fleet of UAVs

Belkadi, Adel; Abaunza, Hernán; Ciarletta, Laurent; Castillo, Pedro; Theilliol, Didier

doi:10.1109/taes.2019.2906437

Cited by 42 publications

(26 citation statements)

References 31 publications

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“…It is assumed that the information about these moving targets' locations is provided by surveillance assets. 28 The performance of the algorithm is examined in this kind of scenario with the assumption that targets must not fly over the FRs. The heading angles of moving targets are À90 , À60 , À30 , 0 , 45 , 90 with respect to North, and the speeds of targets are 15, 10, 10, 0, 5, 5 ft/s, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, it is not convenient to compare our study with theirs since we do not have such a constraint in our problem definition (on the contrary, targets are expected to be visited repeatedly). As another example, in the literature, 27,28 the results are given for a short mission time. Due to time-varying information values (rewards in their case), re-evaluating the problem after each target visit is not suitable for comparison.…”

Section: Introductionmentioning

confidence: 99%

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Online path planning for unmanned aerial vehicles to maximize instantaneous information

Ergezer

Leblebi̇ci̇oğlu

2021

International Journal of Advanced Robotic Systems

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In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online path planning for unmanned aerial vehicles to maximize instantaneous information

Ergezer

Leblebi̇ci̇oğlu

2021

International Journal of Advanced Robotic Systems

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“…In [29], for moving targets, the formation coverage search method was proposed. The authors in [30][31][32][33] discussed distributed path planning using the PSO algorithm and the designing of the quadrotor control.…”

Section: Related Workmentioning

confidence: 99%

Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021

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Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

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“…In recent decades, path planning has been a research hotspot in the field of unmanned vehicles [2]. Many strategies have been developed for path planning, including the dynamic window approach, potential field method [3], fuzzy logic [4], particle swarm optimization (PSO) [5], simulated annealing (SA) algorithm [6], genetic algorithm (GA) and neural network (NN) [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization

et al. 2020

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Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner. INDEX TERMS Path planning, unmanned vehicles, fuzzy logic (FL), ant colony optimization (ACO).

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Design and Implementation of Distributed Path Planning Algorithm for a Fleet of UAVs

Cited by 42 publications

References 31 publications

Online path planning for unmanned aerial vehicles to maximize instantaneous information

Online path planning for unmanned aerial vehicles to maximize instantaneous information

Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization

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