Research on Formation Path Planning Method and Obstacle Avoidance Strategy for Deep-Sea Mining Vehicles Based on Improved RRT*

Abstract

To enhance the autonomous operation capability of deep-sea mining vehicle formations, this study addresses the issues of slow convergence in formation path planning and insufficient obstacle avoidance flexibility under complex environments by investigating a global path planning and local obstacle avoidance strategy based on an improved RRT algorithm*. Through dynamic elliptical sampling, adaptive goal-biased sampling, safe distance detection, and path smoothing optimization, the efficiency and passability of path planning are improved. For the obstacle avoidance of formation members, a priority determination model incorporating local obstacle avoidance, formation contraction, and transformation is designed, and methods such as Gaussian distribution fan-shaped sampling and trajectory backtracking are proposed to optimize the local planning effect. Simulation results show that this method can effectively improve the path planning quality and obstacle avoidance performance of mining vehicle formations in complex environments. Specifically, when in a longitudinal formation, the maximum inter-vehicle error is approximately 15.1%, and the average error is controlled within 3.5%; when in a triangular formation, the maximum inter-vehicle error is approximately 20%, and the average error is controlled within 4.2%, indicating promising application prospects.

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