Motion Planning and Cooperative Manipulation for Mobile Robots With Dual Arms

被引:18
作者
Sun, Fuchun [1 ]
Chen, Yang [1 ]
Wu, Yangyang [1 ]
Li, Linxiang [2 ]
Ren, Xiaolei [3 ]
机构
[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
[2] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China
[3] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
来源
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE | 2022年 / 6卷 / 06期
关键词
Task analysis; Robots; Planning; Robot kinematics; Explosives; Collision avoidance; Mobile robots; Impedance control; mobile robot with dual arms; motion planning; object detection; tactile force control; task planner;
D O I
10.1109/TETCI.2022.3146387
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With large work space and dexterous manipulation capability, mobile robots with dual arms can be used in complex operation scenes such as active detection, disassembly, spraying, cleaning and assembly, is a promising direction for robot development. In this paper, a hybrid control approach is proposed for a mobile robot with dual arms to fulfil an explosive disposal task. Firstly, the motion planning for mobile base and trajectory planning for dual arms are developed, and a hierarchical task planner is designed using the finite state machine to make the robot system to deal with events sensed by active vision. A rapidly-exploring random tree-based motion planning is developed in task space for the mobile base to realize dynamic collision avoidance, while the trajectory planning approach is proposed in the framework of a dual-arm master-slave coordinated mechanism. Next, for mobile robots to complete complex task such as explosive disposal using two dexterous hands, the impedance control approach with slippage detection is developed by considering slippage tendency and slippage intensity to produce a stable in-hand manipulation. Furthermore, the Faster R-CNN is employed to determine the grasping region for robot manipulation through object detection and learning. Finally, the explosive disposal scene is designed to justify the effectiveness and good performance of the proposed methods.
引用
收藏
页码:1345 / 1356
页数:12
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