Research on trajectory planning method for Camellia oleifera fruit sorting robot based on multi-objective optimization

Abstract

Objective: Solved the problems of poor motion stability and accuracy in the food sorting process of parallel robots. Methods: Based on the analysis of the three degree of freedom food sorting robot system, a method proposed which combined polynomial interpolation and improve multi-objective particle swarm optimization algorithm for Delta robot trajectory optimization. As a parallel robot, the optimization of the shortest operation time, lowest energy consumption, and minimal motion impact were taken as multiple objectives. The improved multi-objective particle swarm optimization algorithm was applied to optimize the polynomial interpolation method, and its performance was validated. Results: The planning trajectory of the proposed planning method in the experiment was smoother and more efficient compared to conventional methods. In the actual selection of Camellia oleifera fruits, the accuracy was ＞99.00%, and the average screening time was 0.620 s. Conclusion: The trajectory planning optimization method proposed in the experiment has improved the sorting efficiency, accuracy, and stability of the Camellia oleifera fruit sorting robot.

Keywords

• parallel robots
• food sorting
• trajectory planning
• polynomial interpolation method
• multi-objective particle swarm optimization algorithm