Obstacle avoidance trajectory planning based on improved particle swarm optimization algorithm

Aiming at the problem of global obstacle avoidance trajectory planning of task space robot, a parameter optimization method is proposed to complete obstacle avoidance. Firstly, the polynomial function for trajectory description is derived, and the trajectory shape is changed by changing the parameter value, so as to avoid obstacles. Secondly, aiming at the minimum increment of robot joint angle and the shortest movement time, the penalty function is used to deal with the obstacle avoidance conditions, the optimization model is established, the problem is transformed into solving the optimal parameters, the exponential curve decreasing and dynamic adjustment strategy are proposed, and the particle swarm optimization algorithm is improved to complete the optimization. Finally, the kinematics modeling and workspace analysis of the robot are completed by Matlab, the trajectory planning results are verified by simulation, and the joint data curve of the robot's real-time motion is drawn at the same time. The results show that the improved particle swarm optimization algorithm optimizes the parameters and uses the quintic polynomial as the trajectory description to complete the obstacle avoidance planning, which improves the convergence speed and obtains the global optimal solution compared with the standard algorithm.