This paper proposes a time-optimal trajectory planning method for high-speed and heavy-load handling robots with kinematic redundancy, aimed at significantly increasing the efficiency of stamping production lines.
Kinematic and dynamic models of the robot are established using the modified Denavit−Hartenberg method and the recursive Newton−Euler algorithm. A dynamics-aware constraint space is then constructed by incorporating controllable transient torque exceedance within predefined safety windows. In addition, a segmented trajectory accuracy regulation strategy is introduced to assign strict or relaxed path-error bounds according to operational phases. Finally, Genetic Algorithms-Sequential Quadratic Programming (GA-SQP) is adopted as a numerical solver to search for the minimum-time feasible trajectory under the coupled kinematic and dynamic constraints.
Physical experiments on the heavy-load robot platform validate that the optimized trajectory can be executed at 22 SPM. Compared with the original 18 SPM condition, the proposed method improves the production cycle rate while satisfying the predefined dynamic constraints. The measured torque responses are consistent with the simulation results, confirming the feasibility and safety of the proposed planning framework.
This study integrates task-level kinematic redundancy, segmented trajectory accuracy regulation and controllable dynamic overload constraints into a unified time-optimal planning model for high-speed heavy-load handling robots. Within this integrated framework, B-spline parameterization and a GA-SQP-based solution strategy with diversity-based initial point screening are used to obtain a minimum-time feasible trajectory while balancing efficiency, accuracy and dynamic safety.
