Path planning for unmanned tracked vehicle swarms is an important research area in autonomous technology. However, the existing path planning methods do not consider soft soil's mechanics properties and load-carrying ability in extreme environments, which will seriously affect the safe passage of vehicle swarms. This paper proposes a path planning method for soft mudflat terrain combining semantic information to solve these problems, in which semantic information is the risk coefficient of vehicles passing through the soil by mapping the soil load-carrying ability. Firstly, this method is based on a grid map that integrates semantic information and utilizes the A-Star algorithm for initial path planning. Then, combined with the attenuation model of the mechanics load-carrying ability under the rheological state of the soft terrain, the soil load-carrying ability after the vehicle passes is re-evaluated. Finally, based on the soil load-carrying ability, the passability is determined, the semantic information on the grid map is updated, and the passability path is re-planted. This method improves the shortcomings of existing path planning strategies that lack soil passability assessment and provides a theoretical basis for path planning technology of unmanned vehicle swarms under extreme ground conditions.
Path Planning on Soft Terrain Incorporates Semantic Information
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2024 ; Shenyang, China September 19, 2024 - September 21, 2024
Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems (4th ICAUS 2024) ; Kapitel : 33 ; 367-376
12.04.2025
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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