Journal of System Simulation
Abstract
Abstract: Existing path planning algorithms often struggle to efficiently explore and generate high-quality trajectories. To address this issue, this paper proposes a path planning algorithm that integrates local-global strategies. By employing the rolling window technique, the global one-time path planning problem is transformed into an iterative process of multiple local planning stages. During the global exploration phase, the rolling window is used to determine high-level path branches and to identify branch waypoints, thereby refining the calculation of local paths. In the local exploration phase, an improved RRT-Connect algorithm is proposed, which combines adaptive circular sampling with dynamic step length to reduce redundant sampling points. Simulation experiments demonstrate that the algorithm can efficiently achieve AUV path search in unknown complex environments. The improved RRT-Connect algorithm features faster convergence, generates shorter paths, and produces paths that are closer to the global path.
Recommended Citation
Meng, Wenlong; Pu, Yanbo; and Gong, Ya
(2026)
"AUV Path Planning Integrating Local-global Strategies in Unknown Environments,"
Journal of System Simulation: Vol. 38:
Iss.
4, Article 4.
DOI: 10.16182/j.issn1004731x.joss.24-0983
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss4/4
First Page
889
Last Page
902
CLC
TP242.6
Recommended Citation
Meng Wenlong, Pu Yanbo, Gong Ya. AUV Path Planning Integrating Local-global Strategies in Unknown Environments[J]. Journal of System Simulation, 2026, 38(4): 889-902.
DOI
10.16182/j.issn1004731x.joss.24-0983
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