Collaborative optimization of robotic spraying trajectory based on dual-population chaotic search particle swarm optimization algorithm

Aiming at the deficiency that the spraying trajectory and joint trajectory do not satisfy the mixed constraints caused by the mapping nonlinearity between Cartesian space and joint space, a collaborative optimization of robotic spraying trajectory based on Dual-population Chaotic Search Particle Swarm Optimization (DCSPSO) algorithm was proposed. The joint angle sequence was constructed according to the pre-selected trajectory feature points. Aiming at the robotic spraying efficiency and motion stability, the multi-objective optimization model of joint trajectory was established. Using the DCSPSO algorithm, the optimization model was solved to obtain the Pareto optimal solution, which made the joint trajectory satisfy the robotic kinematics constraints. Based on the chord error and film thickness error of the theoretical and feedback trajectory, the spraying trajectory error model was established and the quality of optimal solution was verified to make the spraying trajectory satisfy the machining accuracy constraints. The case indicated that DCSPSO algorithm had stronger global and local search ability than classical multi-objective algorithms such as multi-objective genetic algorithm. Trajectory error model could add feature points reasonably, which made the maximum chord height error of the theoretical and feedback trajectories was reduced from 12.619 mm to 1.587 mm, and the maximum film thickness error was reduced from 11.47 μm to 1.18 μm. © 2021, Editorial Department of CIMS. All right reserved.