Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization
Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization
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DOI：
10.1007/s00170-022-08796-y
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发表时间：
2021-07
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影响因子：
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通讯作者：
Yu Yang;Hongze Xu;Shaohua Li;Lingling Zhang;Xiu-ming Yao
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作者：
Yu Yang;Hongze Xu;Shaohua Li;Lingling Zhang;Xiu-ming Yao
有效的运动控制可以实现工业机器人的精确定位和快速运动，显著提高工业生产率。时间最优轨迹优化（TO）是机器人运动控制中备受关注的问题，它可以为运动控制器提供高速、合理的运动参考，从而提高运动效率。本文提出了一种新的通用时间优化策略，即二阶连续多项式插值函数（SCPIF）与具有运动学和动力学限制的减余弦权值粒子群优化（CDW-PSO）相结合，成功地对PUMA 560系列机械臂的运动时间进行了优化。基于给定的位置和时间间隔，SCPIF可以生成六个关节在关节空间中的二阶连续运动轨迹。CDW-PSO算法可以在机器人角位移、角速度、角加速度、角抖动和关节力矩的限制下，进一步搜索出最优的运动时间。通过两个数值实验验证了CDW-PSO算法的泛化能力。在每次试验中分别与随机权值（RW）、恒权值（CW）和线性递减权值（LDW）进行比较，可以体现出CDW的优势。实验结果表明，CDW-PSO算法在收敛速度和收敛解质量方面都优于RW-PSO、CW-PSO和LDW-PSO算法。所提出的时间最优TO策略适用于所有类型的机械臂，并且由于考虑了运动学和动力学限制，优化后的轨迹可以纳入实际机械臂的运动控制器中。
Effective motion control could achieve the accurate positioning and fast movement of industrial robotics to improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics, which could improve motion efficiency by providing high-speed and reasonable motion references to motion controllers. In this study, a new general time-optimal TO strategy, the second-order continuous polynomial interpolation function (SCPIF) combined with the particle swarm optimization with cosine-decreasing weight (CDW-PSO) subject to kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator. The SCPIF could be used to generate the second-order continuous movement trajectories of six joints in joint space based on the assigned positions and time intervals. The CDW-PSO algorithm could further search for the optimal movement time subject to the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque of the manipulator. Two numerical experiments are conducted to illustrate the generalization ability of the CDW-PSO algorithm. The advantage of the CDW would be reflected by comparing with the random weight (RW), the constant weight (CW), and the linearly decreasing weight (LDW), respectively, in each experiment. The experimental results show that the CDW-PSO algorithm would perform better than the RW-PSO, CW-PSO, and LDW-PSO algorithms in terms of the convergence rate and quality of the convergent solution. The proposed time-optimal TO strategy would be applied to all types of manipulators while the optimized trajectories could be incorporated in the motion controllers of the actual manipulators due to considering the kinematic and dynamic limits.