Decentralized Multi-UAV Path Planning Based on Two-Layer Coordinative Framework for Formation Rendezvous

Cheng, Zhekun; Zhao, Liangyu; Shi, Zhongjiao

doi:10.1109/access.2022.3170583

Cited by 23 publications

(9 citation statements)

References 50 publications

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“…Cheng et al [156] proposed a decentralized multi-UAV trajectory planning method for obstacle environments. In this method, the UAV rendezvous trajectory planning problem under constraints is modeled as a non-convex optimal control problem, and then, the consensus protocol and sequential convex programming two-layer collaborative framework are used to solve the UAV formation trajectory.…”

Section: Mathematical Optimization Algorithmmentioning

confidence: 99%

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: Mathematical Optimization Algorithmmentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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“…In [27], a fuzzy approach with a linear complexity level is used to convert the MTSP to several TSPs, then Simulated Annealing (SA) is used to solve each problem. Similarly, Cheng et al [28] decouples the MTSP problem into TSP and solves the subproblems through sequential convex programming. Reference [29] propose a task allocation algorithm based on maximum entropy principle (MEP).…”

Section: Related Workmentioning

confidence: 99%

Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning

Gao

Zhang

2022

Sensors

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In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate the tasks area for each UAV. Then, Independent Deep Reinforcement Learning (IDRL) is used to solve Nash equilibrium to improve UAVs’ collaborations. The proposed reward function is smartly developed to guide UAVs to fly along the path with higher reward value while avoiding the collisions between UAVs during flights. Finally, extensive experiments are carried out to compare our proposed method with other algorithms. Simulation results show that the proposed method can obtain more rewards in the same period of time as other algorithms.

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“…The processing methods for geographical environments mainly include digital simulation, rasterization, and the Voronoi diagram. Based on the elevation map, researchers [1,2] converted it into a digital topographic map for path planning. The authors of [3] designed a method for the feature extraction of three-dimensional maps, which added new features to two-dimensional maps and obtained more information compared to two-dimensional maps.…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Fan,

Li,

Chen

et al. 2024

Drones

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To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

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Decentralized Multi-UAV Path Planning Based on Two-Layer Coordinative Framework for Formation Rendezvous

Cited by 23 publications

References 50 publications

UAV Formation Trajectory Planning Algorithms: A Review

UAV Formation Trajectory Planning Algorithms: A Review

Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

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