Multi-Path Planning Method for UAVs Swarm Purposes

Madridano, Ángel; Al-Kaff, Abdulla; Gomez, David Martin; Escalera, Arturo de la

doi:10.1109/icves.2019.8906381

Cited by 16 publications

(11 citation statements)

References 18 publications

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“…A fast random search tree locus is shown in Figure 10. Madridano et al [26] proposed a multi-trajectory PRM-based planning method by establishing a parameter to define three different modes, so that different UAVs in the UAV formation can achieve different mission goals.…”

Section: Rapidly Exploring Random Treesmentioning

confidence: 99%

“…[14] Information, Environment ARSP Dijkstra 2D S. Ueno and S. J. Kwon [15] Time, Optimality Optimal control Dijkstra 2D R. Aggarwal et al [16] Security LARAC Dijkstra 2D E. D'Amato, M. Mattei, and I. Notaro [17] Environment RVG, Bi-level optimization Dubins 3D J. Yang et al [18] Resources PFIH Floyd 2D F. Zhou and H. Nie [19] Environment Shortest path Floyd 2D B. López et al [20,21] Trajectory Lead-Follow, Multiple applications FM 3D B.-b. Meng et al [22] Environment Task allocation Voronoi + Dijkstra 2D [26] Trajectory Multiple trajectories PRM 2D/3D M. Kothari et al [27] Environment Anytime, Guide rate RRT 2D W. Zu et al [28] Environment pruning RRT 2D J. Huang and W. Sun [29] Environment Greedy strategy, Adaptive step size RRT 3D B. Shi et al [30] Environment Minimum snap, Flight corridor RRT 2D…”

Section: Rapidly Exploring Random Treesmentioning

confidence: 99%

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UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

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Section: Rapidly Exploring Random Treesmentioning

confidence: 99%

Section: Rapidly Exploring Random Treesmentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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“…This unique and fast exploration of the environment allows you to establish a highly scalable path planner for use in swarms of UAVs made up of a variable number of agents. Together with the PRM algorithm in charge of exploration, the A* algorithm is used to generate an optimal solution, in terms of total distance traveled, in such a way that, from the set of possible paths, establish, for each UAV, that path that allows it to reach a destination location traveling as little distance as possible [21,22]. This path planner not only has the advantage of being able to generate an optimal solution for a scalable number of UAVs or, being able to work with information in two or three dimensions of the environment, but also contemplates the possibility of being used for different situations such as: a labeled case, in which the location to which each swarm agent must go is previously known; an unlabeled case, for which the proposed path planner not only generates the paths, but is previously responsible for establishing which combination UAV target locations minimizes the total distance traveled by the swarm by using the Hungarian method; Finally, in order to be able to undertake, in the future, firefighting work optimally with a swarm of UAVs, the proposed path planner, allows to establish the final positions each swarm agent within a specific geometric formation and then establish the set of optimal and safe trajectories so that each UAV reaches its position within the formation.…”

Section: Layer I: Global Path Plannermentioning

confidence: 99%

Software Architecture for Autonomous and Coordinated Navigation of UAV Swarms in Forest and Urban Firefighting

Madridano

Al-Kaff

Flores³

et al. 2021

Applied Sciences

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Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. In addition, the expansion of this market has been accompanied by the birth and growth of the so-called UAV swarms. Currently, the expansion of these systems is due to their properties in terms of robustness, versatility, and efficiency. Along with these properties there is an aspect, which is still a field of study, such as autonomous and cooperative navigation of these swarms. In this paper we present an architecture that includes a set of complementary methods that allow the establishment of different control layers to enable the autonomous and cooperative navigation of a swarm of UAVs. Among the different layers, there are a global trajectory planner based on sampling, algorithms for obstacle detection and avoidance, and methods for autonomous decision making based on deep reinforcement learning. The paper shows satisfactory results for a line-of-sight based algorithm for global path planner trajectory smoothing in 2D and 3D. In addition, a novel method for autonomous navigation of UAVs based on deep reinforcement learning is shown, which has been tested in 2 different simulation environments with promising results about the use of these techniques to achieve autonomous navigation of UAVs.

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“…Once these costs are known, the well-known primal-dual Hungarian algorithm (Kuhn 1955;Munkres 1957) can be applied to find the optimal assignment minimizing the total cost. Several multi-robot goal assignment and path planning algorithms Madridano et al 2019;Turpin et al 2014;Hönig et al 2018) do compute the costs for all robot-goal pairs as an integral part of the algorithm. An efficient way of computing these costs is to employ Dijkstra's shortest path algorithm (Dijkstra 1959) for each robot to find the shortest paths from its initial location to all the goal locations.…”

Section: Introductionmentioning

confidence: 99%

It Costs to Get Costs! A Heuristic-Based Scalable Goal Assignment Algorithm for Multi-Robot Systems

Aakash

Saha

2022

ICAPS

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The goal assignment problem for a multi-robot application involves assigning a unique goal to each robot to minimize the total cost of movement for all the robots. A significant step in the state-of-the-art algorithms solving this problem is to find the cost associated with each robot-goal pair. For a large multi-robot system with many robots and many goals in a complex workspace, the computation time required to find the paths for all robot-goal pairs may become prohibitively large. We present an algorithm that solves the optimal goal assignment problem without computing the paths between all the robot-goal pairs. Instead, our algorithm computes the obstacle-free optimal path for any robot-goal pair in a demand-driven way, i.e., if it is absolutely required to ensure the optimality of the goal assignment. We evaluate our algorithm extensively on both randomly generated and standard workspaces for hundreds of robots. Our experimental results demonstrate that the proposed algorithm achieves an order-of-magnitude speedup over the state-of-the-art baseline algorithm. To the best of our knowledge, our algorithm is the first one to solve the multi-robot optimal goal assignment problem without computing the paths for all robot-goal pairs explicitly, guaranteeing high scalability.

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Multi-Path Planning Method for UAVs Swarm Purposes

Cited by 16 publications

References 18 publications

UAV Formation Trajectory Planning Algorithms: A Review

UAV Formation Trajectory Planning Algorithms: A Review

Software Architecture for Autonomous and Coordinated Navigation of UAV Swarms in Forest and Urban Firefighting

It Costs to Get Costs! A Heuristic-Based Scalable Goal Assignment Algorithm for Multi-Robot Systems

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