Zhang Yichen, Han Yu, Qiu Binbin
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China.
Front Neurorobot. 2024 May 22;18:1406604. doi: 10.3389/fnbot.2024.1406604. eCollection 2024.
Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in three-dimensional scenes, which can meet more complex work requirements. Therefore, this study establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To address the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally solves the problem of calculation accuracy which cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Finally, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm is verified by a numerical simulation analysis and two control cases of dual-arm robots.
尽管关于机器人重复运动控制的研究众多，但涉及三维场景中受约束双臂机器人姿态协同运动控制的方案和算法却很少，而这种控制能满足更复杂的工作需求。因此，本研究分别为左右机器人手臂建立了最小位移重复运动控制方案。在此基础上，阐述了所提出的结合新的关节极限转换策略的双臂姿态协同运动控制（DAPCMC）方案的设计思路，随后将该方案转化为一个时变方程系统（TVES）问题形式。为解决TVES问题，设计了一种新颖的自适应泰勒型离散递归神经网络（ATT-DRNN）算法，从根本上解决了计算精度与快速收敛速度无法很好平衡的问题。然后，严格的理论分析证实了ATT-DRNN算法在计算精度和收敛速度方面的可靠性。最后，通过数值模拟分析和双臂机器人的两个控制案例验证了DAPCMC方案的有效性以及ATT-DRNN算法出色的收敛能力。
