基于改进自适应粒子群算法的机器人逆解研究

为提高结构复杂、自由度较高机器人逆运动学求解的准确性,提出了一种改进的自适应粒子群算法(IAPSO).首先,通过改进DH(Denavit-Hartenberg)参数法建立了6自由度臂型抓取机器人模型的运动学方程;其次,在已有粒子群算法的基础上,利用种群曼哈顿距离实时判定种群的进化状态,并根据进化状态的不同确定自适应学习...

Research on Robot Inverse Kinematics Based on Improved Adaptive Particle Swarm Algorithm

In order to improve the accuracy of the inverse kinematics solution of robots with complex structures and high degrees of freedom,an improved adaptive particle swarm optimization algorithm(IAPSO)is proposed.First,the kinematics equation of the six-degree-of-freedom arm robot model is established by improving the DH(Denavit-Hartenberg)parameter method;second,based on the existing particle swarm algorithm,the population Manhattan distance is used to determine the evolution state of the population in real time,and Determine the adaptive learning factor according to the different evolution states,and then use different position and speed update modes;Finally,the fitness function with a penalty factor is introduced to test the robot model for single-point and continuous trajectory inversion solutions,and the error is less than 0.005 rad Joint output.The simulation results show that the established kinematics model is correct,and the improved algorithm takes into account the accuracy,rapidity and uniqueness of the existing PSO algorithm to find the inverse solution,and it has higher solution accuracy.