Multi-Objective Trajectory Optimization of Container Material-Handling Robot

To address the collaborative optimization of efficiency, stability, and energy consumption in container part-handling operations of material-handling robots, this paper proposes a multi-objective trajectory-planning method. First, the kinematic and dynamic models of the robot are established based on the improved D-H parameter method and Lagrange method, with the coordinates of key interpolation points and joint angles in handling operations clarified. Subsequently, the 3-5-3 hybrid polynomial interpolation method is adopted to generate the trajectory. Optimizing the objectives of minimum time, minimum jerk, and minimum energy consumption, an improved particle swarm optimization (IPSO) algorithm dynamically adjusts the inertia weight and learning factor for trajectory optimization. The results show that the convergence speed of the IPSO algorithm increases by 39.6% on average, and the fitness value reduces by 12.7% on average. Experimental validation of joint trajectory optimization demonstrated maximum positional errors of approximately 0.0049 rad, 0.0005 rad, 0.005 rad, and 0.0049 rad for the four joints, with the experimental trajectory closely matching the planned trajectory. Finally, the effectiveness of the scheme is verified by MATLAB 2019 and Adams simulation. Under the time–jerk–energy optimization strategy, the joint trajectory is continuous and smooth, with the peak jerk reduced by 30–40% and the peak torque reduced by 5–10%. The comprehensive performance is superior to the single-objective and dual-objective optimization strategies. This research provides technical support for the efficient and stable operation of the handling robot and provides a reference for the trajectory planning of similar robots.