Xu Xiaohua, Li Peibo, Zhou Jiangwu, Deng Wenzhuo
College of Mechanical Engineering, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2025 Apr 18;25(8):2558. doi: 10.3390/s25082558.
In view of the large randomness, redundant path nodes, and low search efficiency of RRT-connect in a complex obstacle environment, this study intends to develop a path-planning method combining RRT-connect and Informed RRT*. First, to solve the problem of large sampling randomness, the Informed RRT* algorithm is combined to adopt a simpler rectangle and limit the sampling range to the rectangle. Second, for the poor quality of the search path, the dynamic step size is used for growth extension, the reverse greedy algorithm is used to delete redundant nodes, the spline curve is used to smooth the path such that the position meets the cubic spline curve and the speed meets the quadratic spline curve, and the final path is optimized. Finally, the proposed algorithm is verified in the simulation and real world using a self-developed quadrupedal robot. Compared with the original RRT-connect algorithm, the first solution time, total number of nodes, and initial path cost were reduced by more than 11%, 8.5%, and 2.5%, respectively.
针对RRT-connect算法在复杂障碍物环境下随机性大、路径节点冗余以及搜索效率低的问题，本研究旨在开发一种结合RRT-connect和Informed RRT的路径规划方法。首先，为解决采样随机性大的问题，结合Informed RRT算法采用更简单的矩形并将采样范围限制在该矩形内。其次，针对搜索路径质量差的问题，采用动态步长进行生长扩展，使用反向贪婪算法删除冗余节点，利用样条曲线对路径进行平滑处理，使位置满足三次样条曲线且速度满足二次样条曲线，最终对路径进行优化。最后，利用自主研发的四足机器人在仿真和实际场景中对所提算法进行验证。与原始RRT-connect算法相比，首次求解时间、节点总数和初始路径成本分别降低了11%以上、8.5%和2.5%。
