基于离散采样的多模态四足机器人路径规划

doi:10.13229/j.cnki.jdxbgxb.20240155

摘要/Abstract

摘要：

针对双向快速扩展随机树在多模态四足机器人路径规划中存在不必要的跳跃和行走部分路径的地形起伏程度大及转向角度变化大的问题，提出一种基于离散采样的解决方案，预处理路径去除不必要的跳跃，离散采样并动态规划获得粗解，使用B样条曲线拟合并二次规划得到最终路径。仿真结果表明：本文方法规划出的路径使机器人对质心高度的调节平均减少了31.4%，途径地形的起伏程度减小13.4%，地形倾斜角度变化降低11.4%，转向角度变化减小62.7%，证明了本文方法的有效性。

关键词: 人工智能, 四足机器人, 多模态, 路径规划, 离散采样, 二次规划

Abstract:

Aiming at the challenges of unnecessary leap and significant undulations terrains with large steering angles in path planning of multimodal quadruped robots by Rapidly Exploring Random Tree algorithm， a path planning algorithm solution based on discrete sampling is proposed. The path is preprocessed to remove unnecessary leap and a solution set is obtained by discrete sampling and dynamic programming method. B-spline curves are used to define spline segments and quadratic programming method is used to optimize the final path. The simulation results show that paths planned by the proposed method exhibit an average reduction of 31.4% in the adjustment of robot's center of mass height， a 13.4% decrease in undulation of terrain， an 11.4% reduction in terrain slope angle and a 62.7% reduction in steering angle， which affirm the effectiveness of the proposed method.

Key words: artificial intelligence, quadruped robot, multimodal, path planning, discrete sampling, quadratic programming

中图分类号:

TP242

孙帅帅,冯春晓,张良. 基于离散采样的多模态四足机器人路径规划[J]. 吉林大学学报(工学版), 2025, 55(11): 3736-3744.

Shuai-shuai SUN,Chun-xiao FENG,Liang ZHANG. Path planning for multimodal quadruped robots based on discrete sampling[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(11): 3736-3744.

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参考文献 21

[1]	张秀丽, 王琪, 黄森威, 等. 一种多模型融合的仿猎豹四足机器人复杂运动控制方法[J]. 机器人, 2022, 44(6): 682-693.
	Zhang Xiu-li, Wang qi, Huang Sen-wei, et al. A multi-model fusion based complex motion control approach for a cheetah-mimicking quadruped robot[J]. Robot, 2022, 44(6): 682-693.
[2]	张帅帅, 荣学文, 李贻斌, 等. 崎岖地形环境下四足机器人的静步态规划方法[J]. 吉林大学学报: 工学版, 2016, 46(4): 1287-1296.
	Zhang Shuai-shuai, Rong Xue-wen, Li Yi-bin, et al. Static gait planning method for quadruped robots on rough terrains[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(4): 1287-1296.
[3]	周坤, 李川, 李超, 等. 面向未知复杂地形的四足机器人运动规划方法[J]. 机械工程学报, 2020, 56(2): 210-219.
	Zhou Kun, Li Chuan, Li Chao, et al. Motion planning method for quadruped robots walking on unknown rough terrain[J]. Journal of Mechanical Engineering, 2020, 56(2): 210-219.
[4]	Nguyen Q, Powell M J, Katz B, et al. Optimized jump on the mit cheetah 3 robot[C]∥International Conference on Robotics and Automation, Montreal, Canada, 2019: 7448-7454.
[5]	Jenelten F, He J, Farshidian F, et al. DTC: deep tracking control[J]. Science Robotics, 2024, 9(86): eadh5401.
[6]	崔炜, 朱发证. 机器人导航的路径规划算法研究综述[J]. 计算机工程与应用, 2023, 59(19): 10-20.
	Cui Wei, Zhu Fa-zheng. Review of path planning algorithms for robot navigation[J]. Computer Engineering and Applications, 2023, 59(19): 10-20.
[7]	Lavalle S. Rapidly-exploring random trees: a new tool for path planning[J]. The Annual Research Report, 1998, 6: 25989663.
[8]	Kavraki L E, Svestka P, Latombe J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE transactions on Robotics and Automation, 1996, 12(4): 566-580.
[9]	Fankhauser P, Hutter M. A universal grid map library: Implementation and use case for rough terrain navigation[J]. Robot Operating System (ROS) The Complete Reference, 2016, 1: 99-120.
[10]	Fankhauser P, Bloesch M, Gehring C, et al. Robot-centric elevation map with uncertainty estimates[C]∥ 17th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Poznan, Poland, 2014: 433-440.
[11]	张慧, 荣学文, 李贻斌, 等. 四足机器人地形识别与路径规划算法[J]. 机器人, 2015, 37(5): 546-556.
	Zhang hui, Rong Xue-wen, Li Yi-bin, et al. Terrain recognition and path planning for quadruped robot[J]. Robot, 2015, 37(5): 546-556.
[12]	Wermelinger M, Fankhauser P, Diethelm R, et al. Navigation planning for legged robots in challenging terrain[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, South Korea, 2016: 1184-1189.
[13]	Karaman S, Frazzoli E. Sampling-based algorithms for optimal motion planning[J]. The International journal of Robotics Research, 2011, 30(7): 846-894.
[14]	Norby J, Johnson A M. Fast global motion planning for dynamic legged robots[C]∥IEEE/RSJ International Conference on Intelligent Robots and System, Las Vegas, USA, 2020: 3829-3836.
[15]	Kuffner J J, LaValle S M. RRT-connect: an efficient approach to single-query path planning[J]. IEEE International Conference on Robotics and Automation. Symposia Proceedings, 2000, 2: 995-1001.
[16]	Wellhausen L, Hutter M. Rough terrain navigation for legged robots using reachability planning and template learning[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, Canada, 2021: 6914-6921.
[17]	Fernbach P, Tonneau S, Del Prete A, et al. A kinodynamic steering-method for legged multi-contact locomotion[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, Canada, 2017: 3701-3707.
[18]	吴振宇, 刘小飞, 王义普. 基于DKRRT*-APF算法的无人系统轨迹规划[J]. 吉林大学学报: 工学版, 2023, 53(3): 781-791.
	Wu Zhen-yu, Liu Xiao-fei, Wang Yi-pu. Trajectory planning of unmanned system based on DKRRT*⁃APF algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(3): 781-791.
[19]	Deboor C. On calculating with B-splines[J]. Journal of Approximation Theory, 1972, 6(1): 50-62.
[20]	Steinbeck M, Koschke R. Tinyspline: a small, yet powerful library for interpolating, transforming, and querying nurbs, b-splines, and bézier curves[C]∥IEEE International Conference on Software Analysis, Evolution and Reengineering, Honolulu, USA, 2021: 572-576.
[21]	Stellato B, Banjac G, Goulart P, et al. OSQP: an operator splitting solver for quadratic programs[J]. Mathematical Programming Computation, 2020, 12(4): 637-672.

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参数	数值/m
地图长度	12.4
地图宽度	12.4
地图网格分辨率	0.2
初始质心相对地面的高度	0.3