基于改进PSO-GSA算法的时间最优轨迹规划
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1.湖北工业大学机械工程学院 武汉 430068; 2.湖北省现代制造质量工程重点实验室 武汉 430068; 3.湖北时瑞达重型工程机械有限公司 襄阳 441100

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TN102

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Time optimal trajectory planning based on improved PSO-GSA algorithm
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1.School of Mechanical Engineering, Hubei University of Technology,Wuhan 430068, China; 2.Hubei Key Lab of Manufacture Quality Engineering,Wuhan 430068, China; 3.Hubei Shi Ruida Heavy Construction Machinery Co., Ltd., Xiangyang 441100, China

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    摘要:

    传统3-5-3多项式插值轨迹规划算法速度和加速度规划过于保守,与机械臂运动极限条件相差较远,没有充分发挥其运动性能,从而导致机械臂完成任务的时间增长。针对上述问题,本文提出一种基于改进PSO-GSA算法的3-5-3多项式插值轨迹规划算法。首先引入自适应惯性权重与动态学习因子对PSO-GSA算法进行改进,然后使用改进算法对3-5-3多项式插值算法进行时间优化。在优化过程中,关节速度超速时的粒子组使用了与不超速时不同的适应度函数,引导粒子组朝关节速度减小的方向靠拢,加快了算法收敛速度。仿真结果表明,改进的PSO-GSA算法相比原算法及一些同类算法收敛速度更快、搜索精度更高、不易陷入局部最优。对3-5-3多项式插值轨迹规划法进行时间优化后相比优化前运行时间缩短了22.9%,得到的轨迹满足速度限制且平滑稳定,运行更加安全高效。

    Abstract:

    The speed and acceleration planning of the traditional 3-5-3 polynomial interpolation trajectory planning algorithm is too conservative, which is far from the motion limit conditions of the manipulator, and does not give full play to its motion performance, which leads to the increase of the time for the manipulator to complete the task. To solve the above problems, this paper proposes a 3-5-3 polynomial interpolation trajectory planning algorithm based on improved PSO-GSA algorithm. Firstly, the adaptive inertia weight and dynamic learning factor are introduced to improve the PSO-GSA algorithm, and then the improved algorithm is used to optimize the time of 3-5-3 polynomial interpolation algorithm. In the optimization process, the particle group with overspeed used a different fitness function than the one without overspeed, which led the particle group to move closer to the direction of the joint speed decrease, and accelerated the convergence speed of the algorithm. The simulation results show that the improved PSO-GSA algorithm has faster convergence speed, higher search accuracy and is not easy to fall into local optimum than the original algorithm and some similar algorithms. The running time of the 3-5-3 polynomial interpolation trajectory planning method is reduced by 22.9% compared with that before optimization. The obtained trajectory meets the speed limit and is smooth and stable, and the operation is safer and more efficient.

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游达章,赵恒毅,汪传文.基于改进PSO-GSA算法的时间最优轨迹规划[J].电子测量技术,2024,47(12):44-51

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  • 在线发布日期: 2024-11-04
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