改进PSO算法融合人工势场法的工业机器人路径规划设计

李斌, 杨豪中, 甘旭升, 李琦

系统科学与数学 ›› 2021, Vol. 41 ›› Issue (4) : 939-952.

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系统科学与数学 ›› 2021, Vol. 41 ›› Issue (4) : 939-952. DOI: 10.12341/jssms20270

改进PSO算法融合人工势场法的工业机器人路径规划设计

    李斌1,杨豪中2,甘旭升3,李琦4
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Path Planning Design of Industrial Robots Based on Improved PSO Algorithm and Artificial Potential Field Method

    LI Bin1 ,YANG Haozhong2 ,GAN Xusheng3 ,LI Qi4
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摘要

良好的路径规划可以极大提高工业机器人的工作效率. 针对工业机器人与障碍物的干涉判断问题,提出基于轴角的干涉判断方法; 利用栅格法进行环境建模,将机器人路径规划问题转化为可供处理的抽象空间; 结合干涉判断,找出障碍点的范围,并作膨化处理;利用改进的粒子群融合人工势场法的路径规划方法, 构建引力场和斥力场模型,用于生成初始粒子群;提出牵引操作, 解决粒子群算法容易陷入局部最优的问题;利用贝塞尔曲线来进行平滑操作, 解决栅格环境中路径折点多,不利于机器人行走的问题.通过实例证明, 该方法能够克服传统算法和智能算法在机器人路径规划中的不足,且具有较强的搜索能 力和收敛能力.

Abstract

Good path planning can greatly improve the working efficiency of industrial robots. To solve the problem of interference judgment between industrial robots and obstacles, an interference judgment method based on axial angle is proposed. Combined with interference judgment, the range of the obstacle point was found and expanded. Based on the improved path planning method of particle swarm fusion artificial potential field method, the gravitational field and repulsive force field models are constructed to generate the initial particle swarm. The traction operation is proposed to solve the problem that particle swarm optimization is easy to fall into local optimal. Bessel curve is used for smoothing operation to solve the problem that path points in grid environment are too many, which is not conducive to robot walking. An example shows that this method can overcome the shortcomings of traditional and intelligent algorithms in robot path planning, and it has strong searching ability and convergence ability.

关键词

机器人路径规划,干涉判断,粒子群算法,人工势场,路径平滑.

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李斌, 杨豪中, 甘旭升, 李琦. 改进PSO算法融合人工势场法的工业机器人路径规划设计. 系统科学与数学, 2021, 41(4): 939-952. https://doi.org/10.12341/jssms20270
LI Bin, YANG Haozhong, GAN Xusheng, LI Qi. Path Planning Design of Industrial Robots Based on Improved PSO Algorithm and Artificial Potential Field Method. Journal of Systems Science and Mathematical Sciences, 2021, 41(4): 939-952 https://doi.org/10.12341/jssms20270
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