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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots
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Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

机译：具有时变约束的基于神经动力学方法的双臂CMG方案应用于类人机器人

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摘要

We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.
机译：我们提出了一种基于神经动力学方法的双臂循环运动产生（DACMG）方案，该方案可以纠正类人机器人的关节角漂移现象。具体地，根据神经动力学设计方法，首先，开发并应用循环运动性能指标。然后，将此循环运动性能指标集成到具有时变约束的二次编程（QP）型方案中，称为时变约束DACMG（TVC-DACMG）方案。该方案包括两个臂的运动学运动方程式和时变的关节极限。该方案不仅可以产生人形机器人的两条手臂的循环运动，还可以控制手臂移动到所需位置。此外，该方案还考虑了避免物理限制。为了解决QP问题，提出了一种递归神经网络，并将其用于获得最优解。计算机仿真和物理实验证明了这种TVC-DACMG方案和神经网络求解器的有效性和准确性。

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