高级检索

融合改进RRT–Connect与APF的路径规划算法

Path Planning Algorithm of Integrating Improved RRT-Connect and APF

  • 摘要: 为提高无人驾驶车辆路径规划的实时性与安全性,提出一种融合改进双向快速扩展随机树(RRT–Connect)和人工势场(APF)的协同优化算法。首先采用动态步长策略,根据节点与障碍物间距自适应调整扩展步长,显著提高路径搜索效率;其次融合APF特性,利用其引力分量引导随机树向目标点方向偏置采样以加快收敛速度,同时借助APF斥力分量实现障碍物规避以增强路径安全性;进而引入双向剪枝策略结合三次B样条曲线优化,有效缩短路径长度并提升轨迹平滑度;特别地,通过改进APF斥力函数增加目标点间距离分量,解决目标点不可达问题,又确保车辆在动态环境中能稳定抵达目标位置。为验证算法有效性,基于机器人操作系统(ROS)搭建仿真平台,在多种复杂障碍物场景下进行测试。结果表明:与基准RRT和RRT–Connect算法,本文提出的融合优化算法通过动态步长策略和采样函数改进,使路径节点数量分别减少约30%和12%,路径长度分别缩短约30%和13%,搜索时间分别降低约50%和3%;经双向剪枝策略和三次B样条曲线的联合优化处理,路径平滑度进一步提升、长度进一步缩短;改进后的斥力函数不仅有效解决了目标点不可达问题,同时提升了算法在动态复杂环境中的实时避障能力。

     

    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 of 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 efficiency. Second, the characteristics of APF were incorporated, where its attractive 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 effectively shorten the path length and improve trajectory smoothness. Particularly, the APF repulsive function was modified by adding a target distance component to address the goal-unreachable issue while ensuring stable arrival at the target position in dynamic environments. To validate the algorithm’s effectiveness, a simulation platform was established 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 RRT and 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 of 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 of 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 of the algorithm in dynamic complex environments.

     

/

返回文章
返回