基于改进粒子群算法的工业机器人轨迹规划

Trajectory Planning of Industrial Robots Based on Improved Particle Swarm Optimization

针对传统工业机器人轨迹规划工作效率低的问题,提出一种多约束条件下的改进粒子群轨迹优化算法。采用4-3-4分段多项式对工业机器人的路径点进行轨迹拟合,运用改进粒子群算法对工业机器人进行时间最优轨迹规划。该方法引入基于混沌映射的惯性权重更新策略、终端弹性机制和交叉机制,使粒子在迭代后期跳出局部最优,平衡了粒子的全局探索能力和局部开发能力。实验结果表明:采用改进粒子群算法对机器人进行轨迹优化,能够使目标函数快速收敛,并获得满足各关节位置、速度和加速度约束的全局最优时间轨迹。与传统方法相比,该方法使得机器人在约束条件下工作时间最短、运行速度最快,在保证运行平稳的前提下较为明显地提升了机器人的工作效率。

Aiming at low efficiency of the traditional industrial robot in trajectory planning,an improved particle swarm trajectory optimization algorithm under multiple constraints was proposed.The 4-3-4 piecewise polynomial was used to fit path points of the industrial robots,and the improved PSO algorithm was used to plan trajectory of the industrial robots.In this method,the inertia weight updating strategy,terminal elasticity mechanism and crossover mechanism based on chaotic mapping were introduced to make the particles jump out of the local optimal in the late iteration,and to balance the global exploration ability and local development ability of the particles.The experimental results show that,using improved PSO algorithm to optimize trajectory of the robot can make objective function converge quickly,and obtain global optimal time trajectory which satisfying constraints of the position,velocity and acceleration of each joint.Compared with the traditional method,this method can make the robot work in the shortest time and at the fastest speed under constrained conditions,and significantly improve working efficiency of the robot on the premise of ensuring a stable operation.