基于工业机器人运动优化的高维多目标和非线性特点,提出一种快速的机器人最优运动规划方法.用关键点序列形成机器人轨迹的稀疏表达,并构造以运动时间间隔为独立变量,加加速度最小化和速度波动抑制为目标函数的最优化问题.针对任务轨迹及优化对算法全局最优求解精度和收敛速度的要求,提出一种自适应模拟退火粒子群优化算法,通过改进Metropolis准则新解引入机制、自适应惯性权重调整及约束强化等方法获得算法的精度和速度之间的平衡.考虑关节速度、加速度、加加速度等不同类型的约束条件,提出采用罚函数法与分离比较法相结合的约束处理策略.研究结果表明:提出的工业机器人运动优化方法提高了机器人运动的平稳性,减小了位置跟踪误差,并且新的优化算法在全局寻优能力、收敛的快速性、精度等方面均有提高.%Considering that multi-axial robot motion optimization is a multi-objective and nonlinear optimization problem,this paper proposed an effective motion planning and optimization method.Firstly,a sequence of feasible via-points formulated a sparse movement representation of workspace path,then a problem corresponding to movement duration optimization was presented to search for minimum fluctuation of velocities as well as minimum jerk.For the characteristics of task-dependent trajectories and the algorithm requirements for keeping a balance between global solution accuracy and convergence speed,an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm was proposed based on movement duration analysis,including improving Metropolis criterion of perturbation,adjusting the inertial weight coefficients and strengthening the constraints during optimization process.A combination of penalty function and divide-and-conquer strategy was utilized to respect different kinds of boundary conditions of the trajectories,velocities,accelerations and jerks.Simulation and experiment results with circledrawing task on the basis of a Staubli TX90 type industrial robot verify that the proposed scheme obtains a more stable movement of all joints and reduces the position tracking errors,on the other hand,ASAPSO has better performance to research the global optimal solution,keeps high accuracy and quickens the convergence speed,compared with standard particle swarm optimization (PSO).
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