A new approach to time-optimal trajectory planning with torque and jerk limits for robot

In this study, a new convex optimization (CO) approach to time-optimal trajectory planning (TOTP) is described, which considers both torque and jerk limits. The key insight of the approach is that the non-convex jerk limits are transformed to linear acceleration constraints and indirectly introduced into CO as the linear acceleration constraints. In this way, the convexity of CO will not be destroyed and the number of optimization variables will not increase, which give the approach a fast computation speed. The proposed approach is implemented on random geometric path of a 6-DOF manipulator. Compared with a similar method, the results show that the torque and jerk limits are addressed by a reasonable increase in the computation time. In addition, the maximum value of joint jerk reduces by over 80% and the joint torque curves are smoother in the comparison, which demonstrates that this approach has the ability to effectively restrain acceleration mutation. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a new convex optimization approach for time-optimal trajectory planning that effectively restrains acceleration mutation and addresses torque and jerk limits by reasonably increasing computation time. The proposed method can reduce joint jerk values by over 80% and produce smoother joint torque curves compared to a similar method.