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Safe and Robust Terrain Vehicle Navigation Based on Risk-Aware Path-Planning and Control
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AI
胡
Z
Z
Y
H
秦
DOI：10.1109/TIV.2026.3657221.png)
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Abstract
En 中文
Path planning and tracking of terrain vehicles are key to navigation safety in the field considering complex working conditions such as uncertain rough surfaces and deformable soil. This paper proposes a hierarchical path-planning and tracking framework to address inherent stochasticity and safety-critical problems in deformable terrain navigation. Firstly, a safe planning method based on distributional reinforcement learning is proposed, where route safety is strengthened by Conditional Value at Risk (CVaR) optimization based on terrain risk evaluation and constraints. Then, the terramechanics establishes the dynamics model for terrain vehicles driving on soft surfaces. A tracking controller based on the Adaptive Prescribed Performance Sliding Mode Control (APPSMC) is developed, where an improved prescribed performance function and a Fuzzy Logic System (FLS) are introduced to estimate the ground vehicle dynamics as well as the environmental disturbances. Simulation conducted on high-fidelity terrain environments shows satisfactory planning and tracking performances. It exhibits engineering transferability for autonomous operations such as planetary exploration and precision agriculture, providing modular design for seamless integration into off-road robotic platforms, and safety-critical tasks in unpredictable, unstructured environments.
Keywords:
Autonomous terrain vehicles
path planning
path tracking
reinforcement learning
sliding mode control
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I
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14.3
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1.2K
Citations:
1.2W
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Cited Papers