Research on Local Obstacle Avoidance Path Planning Algorithm for Autonomous Mining Trucks

Autonomous mining trucks currently face several challenges in local obstacle avoidance path planning within mining environments, including difficulties in close-range identification, delays in vehicle-to-ground communication, and the absence of real-time planning capabilities on the ground. Based on a downscaled optimization strategy, this paper presents a local obstacle avoidance path planning methodology. Following the initial simplification of obstacle and reference path information based on the Frenet coordinate system, an obstacle clustering strategy is introduced to reduce the discreteness of potential path space. The subsequent utilization of a boundary transformation strategy enables the conversion from non-convex constraints in obstacle avoidance path planning into convex constraints, leading to the construction of a quadratic planning model with variables including lateral offset, offset velocity, and offset acceleration. Through solving the optimization function, autonomous local path planning is initiated for mining trucks encountering obstacles. Both simulations and real vehicle experiments confirmed the effectiveness of the proposed method in rapidly generating effective obstacle avoidance paths using global path information, with a planning time of less than 40 milliseconds. Compared to other local path planning methods based on A* search and dynamic programming (DP), this method could improve planning efficiency by 70%-80%, enhancing the obstacle avoidance capability of autonomous mining trucks in mining environments.