基于SA-IPSO的机械臂轨迹规划及仿真

Trajectory planning and simulation of robotic arm based on SA-IPSO

为满足复杂装配场景下机械臂高效且平稳的运行需求,在时间-冲击最优约束下,提出了结合改进粒子群优化(improved particle swarm optimization,IPSO)算法的4-5-4多项式插值轨迹规划方法。首先,基于标准Denavit-Hartenberg(D-H)方法,建立机械臂仿真模型,并进行了运动学求解与验证;其次,针对传统粒子群算法易陷入局部最优的问题,通过种群混沌初始化,引入模拟退火算法(simulated annealing,SA)中的Metropolis准则,优化动态因子对其进行改进,并求解机械臂末端最优多项式插值曲线,通过仿真验证算法的优越性;最后,探究了时间冲击系数对优化效果的影响,当时间系数β1=0.6,冲击系数β2=0.4时,优化效果最佳,且相比优化前,机械臂的工作效率提高27.5%,冲击值降低26.36%。

In order to meet the demand for efficient and smooth operation of the robotic arm in complex assembly scenarios,a 4-5-4 polynomial interpolation trajectory planning method combined with an improved particle swarm optimization(PSO)algorithm is proposed under the time-impact optimality constraint.Firstly,based on the standard Denavit-Hartenberg(D-H)method,a simulation model of the robotic arm is established,and the kinematic solution and verification are carried out.Secondly,for the traditional particle swarm algorithm,which is prone to fall into the problem of local optimality,it is improved through the initialization of population chaos,the introduction of the simulated annealing algorithm Metropolis criterion,optimization of the dynamic factor,and the optimal polynomial interpolating curves at the end of the robotic arm are solved to validate the algorithm’s.Finally,the influence of the time shock factor on the optimization effect is explored,when the time factorβ1=0.6,shock factorβ2=0.4,the optimization effect is the best,and compared with the pre-optimization,the working efficiency of the robotic arm is improved by 27.5%,and the shock value is reduced by 26.36%.