基于多目标约束的机器人复杂轨迹优化方法  

Multi⁃objectives Optimization⁃based Method for ComplexTrajectory Planning of Manipulators

作　　者：王伟[1,2] 徐泽铨[1] WANG Wei;XU Zequan(School of Mechanical Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China;State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]浙江理工大学机械工程学院,杭州310018 [2]浙江大学流体动力与机电系统国家重点实验室,杭州310027

出　　处：《农业机械学报》2023年第11期431-439,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：浙江省自然科学基金项目(LQ22E050022);国家自然科学基金项目(52175032);浙江省“尖兵领雁”重点项目(2023C01180、2022C01101)。

摘　　要：工业及农业领域存在诸多复杂场景,使得机器人常面临由大量非连续离散局部路径组成的复杂轨迹,合理的运动规划是机器人实现预期作业目标的首要基础。本文提出一种基于非支配遗传算法(Non-dominated sorting genetic algorithm,NSGA-Ⅱ)的多目标综合优化方法,算法基于个体的相互支配关系进行分层并引入“拥挤度”指标来表征个体间的差异性,从而为保持遗传过程的种群多样性提供了有力支撑。同时建立了机器人运动学模型并构造了缩短机器人空载路程、运动时间及关节冲击的路径序列优化函数,并在笛卡尔空间与关节空间进行了高阶样条拟合与插值规划,显著提升了空间轨迹的光顺性及几何特性。基于NSGA-Ⅱ生成空间Pareto最优前沿解集,解决了机器人运动时间短、关节冲击小、任务路径优等约束下的多目标优化问题。优化后机器人运动路径长度缩减74%,作业效率提升33.44%,关节稳定性平均可增强50.97%,通过仿真与实验,验证了算法在改善机器人运动效率、连续性和非突变性等方面具有显著效果。In both industrial and agricultural sectors,robots frequently encounter complex scenarios that consist of numerous discontinuous and discrete local paths,forming challenging trajectories.Rational motion planning serves as the primary foundation for robots to achieve their expected operational goals.A multi-objective comprehensive optimization method was proposed based on the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ).The algorithm operated on the principle of hierarchical sorting based on the dominance relationship between individuals,and introduced a“crowding distance”index to characterize the diversity between individuals,thereby providing robust support for maintaining population diversity during the genetic process.Simultaneously,a kinematic model of the robot was established,and a path sequence optimization function was constructed to reduce the robot’s unloaded travel distance,motion time,and joint impact.Higher-order spline fitting and interpolation planning were implemented in Cartesian and joint spaces,significantly enhancing the smoothness and geometric characteristics of the spatial trajectory.The main contribution lied in generating a spatial Pareto optimal frontier solution set based on NSGA-Ⅱ,which effectively solved the multi-objective optimization problem under constraints such as short robot motion time,small joint impact,and optimal task path.After optimization,the robot’s travel path length was reduced by 74%,operational efficiency was improved by 33.44%,and joint stability was enhanced by an average of 50.97%.Through simulation and experimentation,the algorithm’s significant effectiveness in improving robot motion efficiency,continuity,and non-mutability was verified.

关 键 词：串联机器人 复杂轨迹 NSGA-Ⅱ 多目标优化 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]