Dynamic Cooperative Optimisation With Differential Evolution for Trajectory Planning of Robotic Arms

Trajectory planning of a robotic arm has a significant impact on its operational efficiency and success rate. However, due to the complexity of the environment and the vastness of the search space, it often ends up falling into local optima. In this paper, we propose a novel algorithm that combines particle swarm optimisation (PSO) with differential evolution (DE), namely the PSO-DE algorithm, to alleviate the problem. Firstly, the initial path of the robotic arm is represented by spline curves in the joint space. Then, the trajectory optimisation problem of the robotic arm is established, including constraints such as obstacle cost, acceleration cost, torque cost etc. Finally, the PSO-DE algorithm is proposed for optimisation, from which the PSO ensures the search space range through individual collaboration, whereas the DE generates new solutions through individual differentiation with local search. The combination of the two algorithms can fully leverage their respective advantages, ensuring the global optima within a large search space. Experiments are conducted in a simulation environment using the Python Robotics Toolbox and the PyBullet simulation platform. The results demonstrate that the proposed algorithm can effectively plan the trajectory of the robotic arm with significant improvements in success rates compared to the PSO algorithm.