STC-TEB: Spatial-Temporally Complete Trajectory Generation Based on Incremental Optimization

Abstract

In the context of indoor crowd navigation for mobile robots, the generation of spatial-temporally complete trajectories gives the robot a wider range of options, thereby improving the robustness and safety of navigation. Focused on this topic, this letter presents an incremental optimization framework that initially searches for complete trajectories in spatial dimension and subsequently optimizes them iteratively in spatial-temporal dimension. It benefits from a discount factor design to increase obstacle velocities incrementally, an adaptive determination of the discount factor step to ensure optimization robustness and a dragging force mechanism to adjust trajectories according to obstacle velocities at each step. Compared to the baseline algorithm EGO-TEB and Graphic-TEB, simulations and experiments conducted in lobbies and corridors, with both pedestrians and static obstacles, demonstrate that the proposed STC-TEB algorithm achieves the highest scenario success rate, the highest trajectory completeness rate, nearly the shortest time to the goal, and competitive optimization time.