基于改进RRT*算法的仓储机器人路径规划优化方法
闫转英1 李奇2（通讯作者）

1浩鲸云计算科技股份有限公司，江苏南京，210000；

2南京工业大学浦江学院计算机与通信工程学院，江苏南京，211200；

摘要：针对RRT算法在室内环境中路径规划时存在收敛速度慢、轨迹曲折等问题，本文提出一种结合目标导向采样、自适应步长与贝塞尔曲线平滑的改进方法。该方法在保持RRT渐进最优性的基础上，通过调整采样策略减少无效探索，依据局部障碍密度动态调节扩展步长，并对生成路径进行几何平滑处理。在典型室内场景下的仿真实验表明，与标准RRT*相比，所提方法平均规划时间减少31.2%，路径长度缩短12.5%，转弯次数降低45.1%。结果说明该方法可在有限计算资源下获得更适用于智能车执行的路径。

关键词：路径规划；改进RRT*算法；智能车；采样优化；轨迹平滑

参考文献

[1]Azadeh, K., de Koster, R., & Roy, D. (2019). Robotized and automated warehouse systems: Review and recent developments. Transportation Science, 53(4), 967-999. 

[2]Le-Anh, T., & De Koster, M. B. M. (2006). A review of design and control of automated guided vehicle systems. European Journal of Operational Research, 171(1), 1-23.

[3]Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107. 

[4]Yap, P., Burch, N., & Holte, R. (2011). Dynamic route planning in video games. In Proceedings of the Seventh Conference on Artificial Intelligence and Interactive Digital Entertainment (pp. 142-147). AAAI Press.

[5]Sturtevant, N. R. (2012). Benchmarks for grid-based pathfinding. IEEE Transactions on Computational Intelligence and AI in Games, 4(2), 144-148.

[6]Liu, B., He, L., & Atkin, J. A. (2018). Multi-objective path planning for automated guided vehicles with energy consumption and travel time trade-offs. International Journal of Production Research, 56(15), 5117-5133.

[7]Van den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multi-agent navigation. In 2008 IEEE International Conference on Robotics and Automation (pp. 1928-1935).

[8]Phillips, M., & Likhachev, M. (2011). SIPP: Safe interval path planning for dynamic environments. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 5628-5635). 

[9]Kim, J., Zhang, S., & Sun, C. (2017). Trajectory smoothing and velocity planning for autonomous vehicle navigation. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2446-2455. 

[10]Rosmann, C., Hoffmann, F., Bertram, T., & Knoth, O. (2017). Trajectory modification considering dynamic constraints of autonomous robots. In ROBOTIK 2017; 10th German Conference on Robotics (pp. 1-8). VDE. 

[11]Dehghan, M., & Lee, C. G. (2021). Simulation-based optimization for warehouse robot picking systems. Computers & Industrial Engineering, 160, 107573.

[12]Wang, Y., & Li, X. (2020). Energy-efficient path planning for autonomous mobile robots in manufacturing systems. Journal of Manufacturing Systems, 57, 286-296.