考虑足地作用的足式机器人环境表征与路径规划

doi:10.3901/JME.2020.23.021

机械工程学报 ›› 2020, Vol. 56 ›› Issue (23): 21-33.doi: 10.3901/JME.2020.23.021

扫码分享

考虑足地作用的足式机器人环境表征与路径规划

徐鹏, 丁亮, 高海波, 周如意, 李楠, 邓宗全

哈尔滨工业大学机器人技术与系统国家重点实验室哈尔滨 150080

收稿日期:2020-02-12 修回日期:2020-05-20 出版日期:2020-12-05 发布日期:2021-01-11
作者简介:徐鹏,男,1996年出生。主要研究方向为足式机器人地面感知、运动规划、决策。E-mail:pengxu_hit@163.com;丁亮(通信作者),男,1980年出生,博士,教授,博士研究生导师。主要研究方向为星球车、足式机器人、地面力学与仿真。E-mail:liangding@hit.edu.cn;高海波,男,1970年出生,博士,教授,博士研究生导师。主要研究方向为特种机器人技术,宇航空间机构及控制。E-mail:gaohaibo@hit.edu.cn
基金资助:
国家自然科学基金优秀青年科学基金（51822502）、国家自然科学基金重大研究计划重点支持项目（91948202）、“跃升计划”（HIT.BRETIV.201903）和“111计划”（B07018）资助项目。

Environmental Characterization and Path Planning for Legged Robots Considering Foot-terrain Interaction

XU Peng, DING Liang, GAO Haibo, ZHOU Ruyi, LI Nan, DENG Zongquan

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080

Received:2020-02-12 Revised:2020-05-20 Online:2020-12-05 Published:2021-01-11

摘要/Abstract

摘要： 足式机器人在行走过程中，足端与地面之间的相互作用影响机器人的地面通过情况。足地作用与地表的几何形状和地面的物理特性息息相关，因此仅基于几何特性地图进行路径规划难以满足野外环境下规避松软沙土等非几何危险的需求。针对该问题，考虑足地作用力学提出包含几何与物理特性的环境模型进行足式机器人路径规划。通过简化和统一软硬地面下的足地作用模型，提出表征地面法向松软特性和切向摩擦特性的参数化指标，结合几何特性构建更全面的环境模型。综合考虑影响机器人通过性的地面几何与物理特征，重构路径规划的优化目标，通过图搜索算法实现最优路径规划。以六足机器人Elspider为对象进行仿真和试验，验证了所提出的方法能够有效规避非几何危险，实现了更安全、通过性更强的路径规划。

关键词: 环境表征, 物理特征, 足式机器人, 路径规划

Abstract: During the walking of a legged robot, the interactions between its end of feet and the terrain strongly affect the traversability of the robot. Foot-terrain interaction is closely related to the geometry of the terrain surface and the physical characteristics of it. Therefore, path planning based on geometric map alone is difficult to meet the demand of avoiding non-geometric hazards such as soft sand in the wild. To solve this problem, an environment model including geometric and physical characteristics is proposed in consideration of foot-terrain interaction for path planning of legged robots. By simplifying and unifying the foot-terrain interaction model under soft and rigid terrain, parameterized indexes characterizing the normal softness and tangential friction characteristics of the terrain are proposed, and a more comprehensive environment model is constructed by combining geometric characteristics and the proposed physical characteristics. Comprehensively considering the ground geometric and physical characteristics affecting the robot's traversability, the optimization target of path planning is reconstructed and the optimal path is planned for legged robots with graph search algorithm. Simulation and experiments with the hexapod robot Elspider verified that the proposed method can effectively avoid non-geometric hazards and plan a safer and more reliable path to traverse.

Key words: environmental characterization, physical characteristics, legged robot, path planning

中图分类号:

TP242

徐鹏, 丁亮, 高海波, 周如意, 李楠, 邓宗全. 考虑足地作用的足式机器人环境表征与路径规划[J]. 机械工程学报, 2020, 56(23): 21-33.

XU Peng, DING Liang, GAO Haibo, ZHOU Ruyi, LI Nan, DENG Zongquan. Environmental Characterization and Path Planning for Legged Robots Considering Foot-terrain Interaction[J]. Journal of Mechanical Engineering, 2020, 56(23): 21-33.

参考文献

[1] Raibert M,Blankespoor K,Nelson G,et al. Bigdog,the rough-terrain quadruped robot[J]. IFAC Proceedings Volumes,2008,41(2):10822-10825.
[2] Katz B,Di Carlo J,Kim S. Mini cheetah:A platform for pushing the limits of dynamic quadruped control[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE,2019:6295-6301.
[3] Hwangbo J,Lee J,Dosovitskiy A,et al. Learning agile and dynamic motor skills for legged robots[J]. Science Robotics,2019,4(26):eaau5872.
[4] Krotkov,E,Hoffman R. Terrain mapping for a walking planetary rover[J]. IEEE Transactionson Robotics and Automation,1994,10(6):728-739.
[5] Belter D,Łabcki P,Skrzypczyński P. Estimating terrain elevation maps from sparse and uncertain multi-sensor data[C]//2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE,2012:715-722.
[6] Fankhauser P,Bloesch M,Hutter M. Probabilistic terrain mapping for mobile robots with uncertain localization[J]. IEEE Robotics& Automation Letter,2018,3(4):3019-3026.
[7] Angelova A,Matthies L,Helmick D M,et al. Fast terrain classification using variable length representation for autonomous navigation[J]. Proc. 21st IEEE Conf. Comput. Vis. Pattern Recognit.,2007(1):1-8.
[8] Shirkhodaie A,Amrani R,Tunstel E. Soft computing for visual terrain perception and traversability assessment by planetary robotic systems[J]. Smc,2005,2:1848-1855.
[9] Filitchkin P,Byl K. Feature-based terrain classification for littledog[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE,2012:1387-1392.
[10] Filitchkin P. Visual terrain classification for legged robots[D]. Santa Barbara:University of California,2011.
[11] Wu H,Liu B,Su W,et al. Optimum pipeline for visual terrain classification using improved bag of visual words and fusion methods[J]. Journal of Sensors,2017(1):1-25.
[12] Chilian A,Hirschmuller H. Stereo camera based navigation of mobile robots on rough terrain[C]//IEEE/RSJ International Conference on Intelligent Robots& Systems. IEEE Press,2009:1.
[13] Wooden D,Malchano M,Blankespoor K,et al. Autonomous navigation for BigDog[C]//2010 IEEE International Conference on Robotics and Automation. IEEE,2010:4736-4741.
[14] Hauser K,Bretl T,Latombe J C,et al. Motion planning for legged robots on varied terrain[J]. The International Journal of Robotics Research,2008,27(11-12):1325-1349.
[15] Jaillet L,Cortés J,Siméon T. Transition-based RRT for path planning in continuous cost spaces[C]//2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE,2008:2145-2150.
[16] Perrin N,Stasse O,Baudouin L,et al. Fast humanoid robot collision-free footstep planning using swept volume approximations[J]. IEEE Transactions on Robotics,2011,28(2):427-439.
[17] Belter D,Wietrzykowski J,Skrzypczyński P. Employing natural terrain semantics in motion planning for a multi-legged robot[J]. Journal of Intelligent& Robotic Systems,2019,93(3-4):723-743.
[18] Ding L,Gao H,Deng Z,et al. Foot-terrain interaction mechanics for legged robots:Modeling and experimental validation[J]. The International Journal of Robotics Research,2013,32(13):1585-1606.
[19] Gao H,Jin M,Ding L,et al. A real-time,high fidelity dynamic simulation platform for hexapod robots on soft terrain[J]. Simulation Modelling Practice and Theory,2016,68:125-145.
[20] Silva M F,Machado J A T,Lopes A M. Modelling and simulation of artificial locomotion systems[J]. Robotica,2005,23(5):595-606.
[21] Zhuang H,Gao H,Ding L,et al. Method for analyzing articulated torques of heavy-duty six-legged robot[J]. Chinese Journal of Mechanical Engineering,2013,26(4):801-812.
[22] Lu D V,Hershberger D,Smart W D. Layered costmaps for context-sensitive navigation[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE,2014:709-715.
[23] Hobson R D. FORTRAN IV programs to determine surface roughness in topography for the CDC 3400 computer[M]. Kansas:University of Kansas,1967.
[24] Hobson R D. Surface roughness in topography:A quantitative approach[J]. Anesthesia Sanalgesia,1972,90(3):51-64.
[25] McKean J,Roering J. Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry[J]. Geomorphology,2004,57(3-4):331-351.
[26] Evans I S. Correlation structures and factor analysis in the investigation of data dimensionality:Statistical properties of the Wessex land surface,England[C]//International Symposium on Spatial Data Handling,Zurich. 1984:98-116.
[27] Schmidt J,Evans I S,Brinkmann J. Comparison of polynomial models for land surface curvature calculation[J]. International Journal of Geographical Information Science,2003,17(8):797-814.

考虑足地作用的足式机器人环境表征与路径规划

Environmental Characterization and Path Planning for Legged Robots Considering Foot-terrain Interaction

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	邓建新, 袁邦颐, 黄秋林, 丁度坤, 辛曼玉, 刘光明. 基于工业机器人的复杂曲面磨抛关键技术综述[J]. 机械工程学报, 2024, 60(7): 1-21.
[2]	张伟民, 徐森生, 张月. 基于改进A^*算法的室内巡检机器人路径规划研究[J]. 机械工程学报, 2024, 60(20): 315-326.
[3]	王晓飞, 许家忠, 丁亮, 黄成, 杨怀广, 刘美军. 趋向性锁链式路径规划算法[J]. 机械工程学报, 2024, 60(19): 53-61.
[4]	徐文琳, 彭羽, 何智成, 姜潮. 复杂路径规划的机构分区运动学拓扑构型设计[J]. 机械工程学报, 2024, 60(11): 62-73.
[5]	闫守鑫, 王伟, 苏鹏飞, 贠超. 大型复杂锻件打磨路径的智能化规划方法[J]. 机械工程学报, 2024, 60(11): 216-225.
[6]	胡林, 杨冬兆, 张新, 章杰, 廖家才. 基于DQP-LMPC的智能车超车换道动态路径规划[J]. 机械工程学报, 2024, 60(10): 171-181.
[7]	聂士达, 刘辉, 廖志昊, 谢雨佳, 项昌乐, 韩立金, 林思豪. 考虑复杂地形的越野环境无人车辆路径规划研究[J]. 机械工程学报, 2024, 60(10): 261-272.
[8]	林晨, 魏洪乾, 荆威, 张幽彤. 汽车功能安全：面向敏感指令攻击场景的自主驾驶车辆路径规划风险缓解控制[J]. 机械工程学报, 2024, 60(10): 302-316.
[9]	普雪洁, 张建辉, 李建勇, 张健楠. 复杂技术系统隐性冲突求解路径规划方法[J]. 机械工程学报, 2024, 60(1): 309-328.
[10]	向红标, 杨大虎, 杨璐, 王收军, 张冕, 黄显, 霍文星. 复杂环境下磁弹性微型游泳机器人的路径规划与识别跟踪[J]. 机械工程学报, 2023, 59(5): 89-99.
[11]	吴宏宇, 牛文栋, 张玉玲, 王树新, 阎绍泽. 水下滑翔机多点探测能耗最低路径的规划方法[J]. 机械工程学报, 2023, 59(5): 156-166.
[12]	朱琦歆, 俞滨, 王春雨, 巴凯先, 孔祥东. 液压驱动单元基于力的阻抗控制系统前馈抗扰控制研究[J]. 机械工程学报, 2023, 59(4): 295-307.
[13]	刘鹏, 贾寒冰, 张雷, 王震坡. 结构化道路下智能汽车自主换道轨迹规划研究[J]. 机械工程学报, 2023, 59(24): 271-281.
[14]	崔钰萍, 郑国磊. 电缆布局设计技术研究进展综述[J]. 机械工程学报, 2023, 59(2): 268-280.
[15]	俞滨, 李化顺, 黄智鹏, 何小龙, 巴凯先, 史亚鹏, 孔祥东. 足式机器人液压驱动关键技术研究综述[J]. 机械工程学报, 2023, 59(19): 81-110.