融合改进RRT-Connect与APF的路径规划算法

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关键词：RRT-Connect;人工势场法；动态步长；自适应采样;无人驾驶;实时避障;运动平滑;路径规划 中图分类号：TP242：;TU411.01 文献标志码：A doi：10.12415/j.issn.1671-7872.24088

Abstract： An optimized path planning algorithm integrating improved bidirectional rapidly-exploring random tree （RRT-Connect）and artificial potential field （APF） was proposed to enhance the real-time performance and safety autonomous vehicles.First， a dynamic step size strategy was adopted to adaptively adjust the expansion step size according to the distance between nodes and obstacles，significantly improving path search eficiency.Second，the characteristics APF were incorporated， where its atractive potential component was utilized to bias sampling towards the target direction for accelerated convergence， while its repulsive potential component was employed to achieve obstacle avoidance for enhanced path safety.Furthermore，a bidirectional pruning strategy combined with cubic B-spline curve optimization was introduced to efectively shorten the path length and improve trajectory smoothness.Particularly，the APFrepulsive function was modified by adding a target distance component to address the goal-unreachable issue while ensuring stable arival at the target position in dynamic environments.To validate the algorithm's efectiveness，a simulation platform was establised based on the robot operating system （ROS），and tests were conducted in various complex obstacle scenarios.The experimental results demonstrate that compared with the benchmark RRTand RRT-Connect algorithms，the proposed integrated optimization algorithm achieves approximately 30% and 12% reduction in path node quantity， 30% and 13% shortening in path length， and 50% and 3% decrease in search time respectively through the improvement dynamic step strategy and sampling function. The path smoothness is further enhanced and the length is additionally reduced after being processed by the combined optimization bidirectional pruning strategy and cubic B-spline curve.The modified repulsive potential function not only effectively solves the goal-unreachable problem but also improves the real-time obstacle avoidance capability the algorithm in dynamic complex environments.

Keywords： RRT-Connect algorithm; artificial potential field （APF） method; dynamic step size; adaptive sampling; autonomous driving; real-time obstacle avoidance; motion smoothing; path planning

汽车无人驾驶技术作为当前科技发展前沿领域历经探索期、技术积累期已进入实际应用阶段，在人工智能技术推动下成为研究热点。（剩余12697字）