广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (06): 53-61.doi: 10.12052/gdutxb.200165

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面向抓取任务的移动机器人停靠位置优化方法研究

王东, 黄瑞元, 李伟政, 黄之峰   

  1. 广东工业大学 自动化学院，广东 广州 510006
  • 收稿日期:2020-12-10 出版日期:2021-11-10 发布日期:2021-11-09
  • 通信作者: 黄之峰(1984–)，男，副教授，博士，主要研究方向为仿人机器人、柔顺关节机器人，E-mail：zhifeng@gdut.edu.cn E-mail:zhifeng@gdut.edu.cn
  • 作者简介:王东(1997–)，男，硕士研究生，主要研究方向为移动机械臂
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(500160075)；广东省自然科学基金资助博士启动项目(2016A030310350)

A Research on Docking Position Optimization Method of Mobile Robot for Grasping Task

Wang Dong, Huang Rui-yuan, Li Wei-zheng, Huang Zhi-feng   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-12-10 Online:2021-11-10 Published:2021-11-09

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摘要： 为了解决移动机器人在复杂环境中物体抓取规划成功率低以及规划时间长等问题, 本文提出了一种基于环境信息的预处理生成移动机器人停靠位置优化算法。首先对机械臂的工作空间进行分析, 得到抓取难易评价标准, 将环境中目标物、障碍物以及移动底盘位置简化为点, 投影到xy平面上, 根据抓取难易评价标准求出移动机器人优化后的底盘停靠位置; 然后针对机械臂避障问题, 采用快速扩展随机树(Rapidly-exploring Random Trees, RRT)算法实现了机械臂末端及连杆与障碍物的避障; 最后通过仿真和动作捕捉系统下的实验发现, 采用移动机器人停靠位置优化算法可显著提高抓取规划成功率和规划速度。

关键词: 移动机器人, 抓取难易评价标准, 停靠位置优化, 快速扩展随机树

Abstract: In order to solve the problems of low success rate and long planning time of mobile robot in complex environment, a mobile robot docking position optimization algorithm is proposed based on environment information preprocessing. Firstly, the workspace of the manipulator is analyzed, and the evaluation criteria of grasping difficulty are obtained. The positions of objects, obstacles and mobile chassis in the environment are simplified as points and projected on the xy plane. According to the evaluation criteria of grasping difficulty, the optimized chassis docking position of the mobile robot is obtained. For the obstacle avoidance problem of the manipulator, the rapidly-exploring random trees (RRT) algorithm is used to realize the robot end, connecting rod and obstacle. Finally, through the simulation and experiments in the motion capture system, it is found that using the parking position optimization algorithm of mobile robot can increase the success rate of grasping objects and the speed of grasping planning .

Key words: mobile robot, evaluation standard of grasping difficulty, docking position optimization, rapidly-exploring random trees

中图分类号: 

  • TP242.6
[1] 孟偲, 王田苗. 一种移动机器人全局最优路径规划算法[J]. 机器人, 2008, 37(3): 217-222.
MENG C, WANG T M. A global optimal path planning algorithm for mobile robot [J]. Robot, 2008, 37(3): 217-222.
[2] 张宏钊, 刘顺桂, 姜勇, 等. 基于可操作度的移动机械臂路径规划研究[J]. 自动化与仪表, 2018, 33(10): 45-49.
ZHANG H Z, LIU S G, JIANG Y, et al. Research on mobile manipulator path planning based on operability [J]. Automation & Instrumentation, 2018, 33(10): 45-49.
[3] GROVES P D. Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd edition [Book review] [J]. IEEE Aerospace and Electronic Systems Magazine, 2015, 30(2): 26-27.
[4] LAU B, SPRUNK C, BURGARD W. Efficient grid-based spatial representations for robot navigation in dynamic environments [J]. Robotics and Autonomous Systems, 2013, 61(10): 1116-1130.
[5] 汪盛民, 林伟, 曾碧. 未知环境下基于虚拟子目标的对立Q学习机器人路径规划[J]. 广东工业大学学报, 2019, 36(1): 51-56.
WANG S M, LIN W, ZENG B. Path planning of opposite Q learning robot based on virtual sub-target in unknown environment [J]. Journal of Guangdong University of Technology, 2019, 36(1): 51-56.
[6] 肖林, 周文辉. 移动机械臂的协调运动方案设计及验证[J]. 中山大学学报(自然科学版), 2016, 55(2): 52-57.
XIAO L, ZHOU W H. Design and verification of coordinated motion scheme for mobile manipulators [J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2016, 55(2): 52-57.
[7] BROCK O, PARK J, TOUSSAINT M. Mobility and manipulation[M]. Berlin:Springer International Publishing, 2016,: 1007-1036.
[8] ISKANDAR M, QUERE G, HAGENGRUBER A, et al. Employing whole-body control in assistive robotics[C]// IEEE International Conference on Intelligent Robots and Systems. Macau: IEEE, 2019: 5643-5650.
[9] KIM S, JANG K, PARK S, et al. Whole-body control of non-holonomic mobile manipulator based on hierarchical quadratic programming and continuous task transition[C]//2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM). Toyonaka: IEEE, 2019: 414-419.
[10] 王洪斌, 尹鹏衡, 郑维, 等. 基于改进的A*算法与动态窗口法的移动机器人路径规划[J]. 机器人, 2020, 42(3): 346-353.
WANG H B, YIN P H, ZHENG W, et al. Mobile robot path planning based on improved A* algorithm and dynamic window method [J]. Robot, 2020, 42(3): 346-353.
[11] 杜峰, 苏丽颖, 焦继乐, 等. 移动机械臂无碰抓取方法的研究[J]. 机械科学与技术, 2016, 35(4): 535-538.
DU F, SU L Y, JIAO J L, et al. A method for collision-free grasp of mobile manipulator [J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(4): 535-538.
[12] 王宪, 杨国梁. 基于改进蚁群算法的机器人轨迹规划[J]. 计算机系统应用, 2010, 19(11): 79-82.
WANG X, YANG G L. Robot trajectory planning based on improved ant colony algorithm [J]. Computer Systems & Applications, 2010, 19(11): 79-82.
[13] 代彦辉, 梁艳阳, 谢钢. 基于RRT搜索算法的六自由度机械臂避障路径规划[J]. 自动化技术与应用, 2010, 19(11): 79-82.
DAI Y H, LIANG Y Y, XIE G. The mechanical arm of six-dof obstacle avoidance path planning based on rapid-exploring random trees [J]. Techniques of Automation and Applications, 2010, 19(11): 79-82.
[14] 陈满意, 张桥, 张弓, 等. 多障碍环境下机械臂避障路径规划[J]. 计算机集成制造系统, 2021, 27(4): 990-998.
CHEN M Y, ZHANG Q, ZHANG G, et al. Research on obstacle avoidance path planning of manipulator in multiple obstacles environment [J]. Computer Integrated Manufacturing Systems, 2021, 27(4): 990-998.
[15] 杜爽, 尚伟伟, 刘坤, 等. 基于双向RRT算法的仿人机器人抓取操作[J]. 中国科学技术大学学报, 2016, 46(1): 12-20.
DU S, SHANG W W, LIU K, et al. Bidirectional RRT algorithm based grasping manipulation of humanoid robots [J]. Journal of University of Science and Technology of China, 2016, 46(1): 12-20.
[16] GUO Y, BENNAMOUN M, SOHEL F, et al. 3D object recognition in cluttered scenes with local surface features: a survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(11): 2270-2287.
[17] GOKBERK C R, VERBEEK J, SCHMID C. Multi-fold mil training for weakly supervised object localization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 2409-2416.
[18] HUANG Z, LI J, HUANG J, et al. Motion planning for bandaging task with abnormal posture detection and avoidance [J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(5): 2364-2375.
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