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Motion planning optimization of trajectory path of space manipulators

机译:空间操纵器轨迹路径的运动规划优化

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With the development of the aerospace industry, the work carried out inside and outside the weightless space station is becoming more and more complicated. In order to ensure the safety of astronauts, space manipulators are used for operation, but it will disturb the space station that is a base during work. In order to solve the above problems, in this paper, the planning method of the motion trajectory of manipulators, the motion model of manipulators and the particle swarm optimization (PSO) algorithm used for optimizing the trajectory are briefly introduced, the multi-population co-evolution method is used to improve the PSO algorithm, and the above two algorithms are used to optimize the motion trajectory of the floating pedestal space manipulator with three free degrees of rotation in the same plane by the matrix laboratory (MATLAB) software. It is compared with genetic algorithm. The results show that the improved PSO algorithm can converge to a better global optimal fitness with fewer iterations compared with the traditional PSO algorithm and genetic algorithm. The obtained motion trajectory optimized by the improved PSO algorithm has less disturbances to the pedestal posture, and less time is required to achieve the target motion; moreover the changes of mechanical arm joint are more stable during the motion.
机译:随着航空航天行业的发展,无失重空间站内外的工作变得越来越复杂。为了确保宇航员的安全性,空间操纵器用于操作,但它会扰乱工作期间基座的空间站。为了解决上述问题,在本文中,简要介绍了用于优化轨迹的操纵器的运动轨迹,操纵器运动模型和粒子群优化(PSO)算法的规划方法,是用于优化轨迹的多种群体CO -evolution方法用于改善PSO算法,上述两种算法用于优化浮动基座空间机械手的运动轨迹,通过矩阵实验室(MATLAB)软件在同一平面中具有三个自由度的自由度。它与遗传算法进行了比较。结果表明,与传统的PSO算法和遗传算法相比,改进的PSO算法可以通过较少的迭代融合到更好的全局最佳健身。通过改进的PSO算法优化的所获得的运动轨迹对基座姿势的干扰较小,并且需要较少的时间来实现目标运动;此外,在运动期间机械臂接头的变化更稳定。

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