DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor

Park, Jungwon; Lee, Yun-Woo; Jang, Inkyu; Kim, H. Jin

doi:10.1109/tro.2023.3279903

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…An optimized particle cluster hybrid ant colony (PSOHAC) algorithm was proposed by Yan [32] to plan the path of a UAV cluster task. Park [33] presented an online distributed trajectory planning algorithm for a quadrotor cluster in a maze-like dynamic environment. Zhang [34] proposed a novel strategy that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration.…”

Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

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Section: Introductionmentioning

confidence: 99%