Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

Wang, Tingzhong; Zhang, Binbin; Zhang, Mengyan; Zhang, Sen

doi:10.1155/2021/6964875

Cited by 8 publications

(7 citation statements)

References 13 publications

(20 reference statements)

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“…C. Xia and A. Yudi [120] Trajectory Dynamic step size, Adaptive learning NN 3D G. Sanna et al [121] Trajectory Supervised learning ANN + A* 2D W. Luo et al [122] Environment Multi-agent Deep-Sarsa 3D H. Qie et al [123] Environment STATP MADDPG 2D C. Zhao et al [124] coverage problem Adaptive, Information sharing DMUCRL 2D T. Wang et al [125] Environment Attention network AM 2D Q. Liu et al [126] Environment, Trajectory SSA, B-spline curve BINN 3D L. Wang et al [127] MEC Low complexity MADDPG 2D W. Zhang et al [128] Communication CMDP cDQN 3D H. Bayerlein et al [129] Collect Data Dec-POMDP DDQN 2D S. Tianle et al [130] Time Note dynamic clustering PSO + IA-DRL 2D…”

Section: Reference Challenge Optimization Criteria Methods Dimensionmentioning

confidence: 99%

“…Wang et al [125] proposed a collaborative trajectory planning method for multiple UAVs based on attentional reinforcement learning. This method uses a neural network with an attention mechanism to generate a UAV cooperative reconnaissance strategy (AM) and uses a reinforcement algorithm to test a large amount of simulation data and optimize the attention network.…”

Section: Reinforce Learningmentioning

confidence: 99%

See 1 more Smart Citation

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: Reference Challenge Optimization Criteria Methods Dimensionmentioning

confidence: 99%

Section: Reinforce Learningmentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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“…Splitting the task of collaborative exploration into two core parts, the first part is sensor-based, and the second part is the path planner assigns actionable tasks to each agent, including the ability to provide reachable collision-free paths and multiple simulations in complex location environments. Wang et al [48] proposed a multi-UAV collaborative path planning method based on reinforcement learning to solve the problem that traditional heuristic algorithms are difficult to extract empirical models from large sample terrain data in time, which comprehensively considers the influencing factors such as survival probability, path length, load balancing and durability constraints. Hao et al [49] proposed a dynamic fast Q-learning (DFQL) algorithm for the path planning problem of USV in some known marine environments, which combines Q-learning with artificial potential field (APF) to initialize the Q table and provides USV with prior knowledge from the environment.…”

Section: Related Workmentioning

confidence: 99%

Optimized-Weighted-Speedy Q-Learning Algorithm for Multi-UGV in Static Environment Path Planning under Anti-Collision Cooperation Mechanism

Cao

Fang

2023

Mathematics

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With the accelerated development of smart cities, the concept of a “smart industrial park” in which unmanned ground vehicles (UGVs) have wide application has entered the industrial field of vision. When faced with multiple tasks and heterogeneous tasks, the task execution efficiency of a single UGV is inefficient, thus the task planning research under multi-UGV cooperation has become more urgent. In this paper, under the anti-collision cooperation mechanism for multi-UGV path planning, an improved algorithm with optimized-weighted-speedy Q-learning (OWS Q-learning) is proposed. The slow convergence speed of the Q-learning algorithm is overcome to a certain extent by changing the update mode of the Q function. By improving the selection mode of learning rate and the selection strategy of action, the relationship between exploration and utilization is balanced, and the learning efficiency of multi-agent in complex environments is improved. The simulation experiments in static environment show that the designed anti-collision coordination mechanism effectively solves the coordination problem of multiple UGVs in the same scenario. In the same experimental scenario, compared with the Q-learning algorithm and other reinforcement learning algorithms, only the OWS Q-learning algorithm achieves the convergence effect, and the OWS Q-learning algorithm has the shortest collision-free path for UGVS and the least time to complete the planning. Compared with the Q-learning algorithm, the calculation time of the OWS Q-learning algorithm in the three experimental scenarios is improved by 53.93%, 67.21%, and 53.53%, respectively. This effectively improves the intelligent development of UGV in smart parks.

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“…The cooperative path planning method for multiple UAVs in a deterministic environment can be regarded as an NP-hard combinatorial optimization problem with many constraints [10]. In recent years, many scholars have conducted extensive research on how to establish mathematical models and solve the NP-hard combinatorial optimization problem [11][12][13][14][15][16][17][18]. The main mathematical models include the multitrip salesman model (MTSP), mixed linear integer programming model (MILP), and vehicle scheduling and path planning model (VRP).…”

Section: Related Workmentioning

confidence: 99%

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Cheng

et al. 2022

International Journal of Aerospace Engineering

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Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.

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Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

Cited by 8 publications

References 13 publications

UAV Formation Trajectory Planning Algorithms: A Review

UAV Formation Trajectory Planning Algorithms: A Review

Optimized-Weighted-Speedy Q-Learning Algorithm for Multi-UGV in Static Environment Path Planning under Anti-Collision Cooperation Mechanism

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

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