Spline-Based Optimal Trajectory Generation for Autonomous Excavator

In this paper, we propose a novel trajectory generation method for autonomous excavator teach-and-plan applications. Rather than controlling the excavator to precisely follow the teaching path, the proposed method transforms the arbitrary slow and jerky trajectory of human excavation into a topologically equivalent path that is guaranteed to be fast, smooth and dynamically feasible. This method optimizes trajectories in both time and jerk aspects. A spline is used to connect these waypoints, which are topologically equivalent to the human teaching path. Then the trajectory is reparametrized to obtain the minimum time-jerk trajectory with the kinodynamic constraints. The optimal time-jerk trajectory generation method is both formulated using nonlinear programming and conducted iteratively. The framework proposed in this paper was integrated into a complete autonomous excavation platform and was validated to achieve aggressive excavation in a field environment.

Automated summary

This paper proposes a novel trajectory generation method for autonomous excavator teach-and-plan applications. It transforms the human excavation trajectory into a topologically equivalent path that is fast, smooth, and dynamically feasible. The method optimizes trajectories in both time and jerk aspects and has been validated in a field environment.