Sampling-based time-optimal path parameterization with jerk constraints for robotic manipulation

Abstract

In this paper, a sampling-based time-optimal path parameterization (S-TOPP) method is proposed to address time-optimal trajectory planning problems with bounded jerks. The key insight of S-TOPP is that a tree of feasible nodes connected by edges is established to find a time-optimal trajectory on the temporal dimension. The tree establishment process includes two major phases at each stage, namely sampling and one-step backtracking. In the sampling phase, a new sampling strategy integrating historical information is proposed to obtain superior samples whereby fewer samples can be controlled automatically, reducing the calculation loss. In one-step backtracking phase, a “lazy” strategy is used to lazily skip constraint-checking when evaluating local connections, enabling S-TOPP to avoid checking the vast majority of nodes that have no chance of being in an optimal trajectory. Simulations and real-world experiments validate the feasibility and practicability of S-TOPP. Results show that S-TOPP is an effective solution to jerk-bounded time-optimal trajectory planning, with features that are more in line with the needs of the practical tasks compared with other methods.