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Jerk-level cyclic motion planning and control for constrained redundant robot manipulators using Zhang dynamics: Theoretics

机译:使用张动力学的受限冗余机器人操纵器急动级循环运动规划与控制

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In this paper, based on Zhang dynamics method (ZDM), a cyclic motion planning (CMP) scheme is developed to remedy the joint drift phenomenon of redundant robot manipulators constrained by joint physical limits. Unlike existing works resolved at velocity level or acceleration level, the scheme in this paper is resolved at jerk level. To achieve superior performance, the jerk-level CMP scheme is improved by the feedback control, which further leads to a jerk-level cyclic motion planning and control (CMPC) scheme. The cyclic motion criterion and the avoidance of joint physical limits (e.g., joint-angle, joint-velocity, joint-acceleration and joint-jerk limits) are both considered into the jerk- level CMP and CMPC schemes. More importantly, the correctness of the presented jerk-level schemes is guaranteed by the corresponding engineering-type theoretical analyses and results. Then, the jerk-level CMPC scheme is reformulated as a dynamical quadratic program (DQP) which can be solved by a piecewise-linear projection equation neural network (PLPENN), and also can be solved by a suitable numerical algorithm.
机译:本文基于张动力学方法(ZDM),提出了一种循环运动规划(CMP)方案,以解决受关节物理极限约束的冗余机器人机械手的关节漂移现象。与现有的工作在速度或加速度级别上解决不同,本文中的方案在加速级别上解决。为了获得优异的性能,通过反馈控制改进了急动级CMP方案,这进一步导致了急动级循环运动计划和控制(CMPC)方案。循环运动标准和避免关节物理极限(例如,关节角度,关节速度,关节加速度和关节跳动极限)都被考虑到跳动级CMP和CMPC方案中。更重要的是,相应的工程类型理论分析和结果保证了所提出的混蛋级方案的正确性。然后,将混动级CMPC方案重新构造为动态二次程序(DQP),可以通过分段线性投影方程神经网络(PLPENN)对其进行求解,也可以通过适当的数值算法对其进行求解。

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