| 引用本文: | 曹博,毕树生,郑晶翔,杨东升,黄国威.改进人工势场法的冗余机械臂避障算法[J].哈尔滨工业大学学报,2019,51(7):184.DOI:10.11918/j.issn.0367-6234.201806121 |
| CAO Bo,BI Shusheng,ZHENG Jingxiang,YANG Dongsheng,HUANG Guowei.Obstacle avoidance algorithm for redundant manipulator of improved artificial potential field method[J].Journal of Harbin Institute of Technology,2019,51(7):184.DOI:10.11918/j.issn.0367-6234.201806121 |
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| 摘要: |
| 针对传统人工势场法应用于串联型冗余机械臂避障时无法约束各关节位姿、陷入局部极小后难以逃离的问题,提出一种改进人工势场法. 建立冗余机械臂运动学模型,采用线段球体包络盒模型进行碰撞检测. 在笛卡尔空间内建立末端引力势场和障碍物斥力势场,在关节空间内建立目标角度引力势场,所有势场共同作用引导机械臂运动. 在关节空间内求解虚拟目标角度并采用高斯函数建立虚拟引力势场处理局部极小问题. 利用七自由度冗余机械臂进行仿真和实验,结果表明:算法可约束各关节位姿,陷入局部极小后可引导机械臂逃离局部极小,最终完成避障；避障结束时各关节角度最大误差为0.8 °,末端平均位置误差和平均姿态误差分别为0.010 m和2.40 °,均小于传统算法；避障过程中各关节运动幅度小于传统算法. 改进算法可引导机械臂逃离局部极小并完成避障,同时提高避障结束时各关节及末端的定位精度,对冗余机械臂的避障研究及应用具有一定的指导意义. |
| 关键词: 冗余机械臂 人工势场法 局部极小 避障 运动规划 碰撞检测 |
| DOI：10.11918/j.issn.0367-6234.201806121 |
| 分类号:TP242.6 |
| 文献标识码:A |
| 基金项目: |
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| Obstacle avoidance algorithm for redundant manipulator of improved artificial potential field method |
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CAO Bo,BI Shusheng,ZHENG Jingxiang,YANG Dongsheng,HUANG Guowei
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（School of Machanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China）
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| Abstract: |
| An improved artificial potential field method (APFM) is proposed for the problem that only the end of manipulator can be guided but the angle of each joint cannot be constrained, and it is difficult to escape from local minimum when traditional APFM is used to avoid obstacles for redundant manipulator. A kinematic model of the manipulator is established and the line segment sphere enveloping box model is established for collision detection. Attract potential field of the end and obstacle repulsive potential field are established in Cartesian, and attract potential field of the target angle is established in joint space, and they work together to guide the manipulator. A virtual target angle is solved in joint space and the virtual potential field is established using Gaussian function to deal with the local minimum problem. The simulations and experiments on the 7 DOF redundant manipulator show that the algorithm can constraint joint pose and guide the manipulator escape from local minimum when trapped, and finally complete obstacle avoidance. At the end of the obstacle avoidance, the maximum error of each joint angle is 0.8°, and the average position error and attitude error are 0.010 m and 2.40°, which are smaller than the traditional algorithm respectively. The motion amplitude of each joint in the obstacle avoidance process is smaller than the traditional algorithm. The improved algorithm can guide the manipulator escape from the local minimum and complete the obstacle avoidance, as well as improve the positioning accuracy of each joint and the end at the end of the obstacle avoidance. The study has certain guiding significance for research and application of obstacle avoidance for redundant manipulators. |
| Key words: redundant manipulators artificial potential field local minimum obstacle avoidance motion planning collision detection |
