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Improved singular robust inverse solutions of redundant serial manipulators
International Journal of Advanced Robotic Systems ( IF 2.3 ) Pub Date : 2020-05-01 , DOI: 10.1177/1729881420932046
Jingyu Sun 1, 2 , Yanjun Liu 1, 2, 3 , Chen Ji 1
Affiliation  

To address the Jacobian matrix approximation error, which usually exists in the iterative solving process of the classic singular robust inverse method, the correction coefficient α is introduced, and the improved singular robust inverse method is the result. On this basis, the constant improved singular robust method and the intelligent improved singular robust inverse method are proposed. In addition, a new scheme, combining particle swarm optimization and artificial neural network training, is applied to obtain real-time parameters. The stability of the proposed methods is verified according to the Lyapunov stability criteria, and the effectiveness is verified in the application examples of spatial linear and curve trajectories with a seven-axis manipulator. The simulation results show that the improved singular robust inverse method has better optimization performance and stability. In the allowable range, the terminal error is smallest, and there is no lasting oscillation or large amplitude. The least singular value is largest, and the joint angular velocity is smallest, exactly as expected. The derivative of the Lyapunov function is negative definite. Comparing the two extended methods, the constant improved singular robust method performs better in terms of joint angular velocity and least singular value optimization, and the intelligent improved singular robust inverse method can achieve a smaller terminal error. There is little difference between their overall optimization effects. However, the adaptability of the real-time parameters makes the intelligent improved singular robust inverse method the first choice for kinematic control of redundant serial manipulators.

中文翻译:

冗余串行机械手的改进奇异鲁棒逆解

针对经典奇异鲁棒逆法迭代求解过程中通常存在的雅可比矩阵逼近误差,引入修正系数α,得到改进的奇异鲁棒逆法。在此基础上,提出了常数改进奇异鲁棒法和智能改进奇异鲁棒逆法。此外,采用结合粒子群优化和人工神经网络训练的新方案来获取实时参数。根据Lyapunov稳定性准则验证了所提方法的稳定性,并在空间线性和曲线轨迹的七轴机械臂应用实例中验证了其有效性。仿真结果表明,改进的奇异鲁棒逆方法具有更好的优化性能和稳定性。在允许范围内,终端误差最小,不存在持续振荡或大振幅。最小奇异值最大,关节角速度最小,完全符合预期。Lyapunov 函数的导数是负定的。比较两种扩展方法,常数改进奇异鲁棒方法在关节角速度和最小奇异值优化方面表现更好,智能改进奇异鲁棒逆方法可以实现更小的终端误差。它们的整体优化效果差别不大。然而,
更新日期:2020-05-01
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