GA-TEB: Goal-Adaptive Framework for Efficient Navigation Based on Goal Lines

TL;DR

Simulations and experiments demonstrate that the proposed GA-TEB framework effectively prevents deadlock situations, where the robot becomes frozen due to a lack of feasible trajectories in crowded environments, and greatly increases planning frequency in scenarios with numerous non-convex obstacles, enhancing both robustness and safety.

Abstract

In crowd navigation, the local goal plays a crucial role in trajectory initialization, optimization, and evaluation. Recognizing that when the global goal is distant, the robot's primary objective is avoiding collisions, making it less critical to pass through the exact local goal point, this work introduces the concept of goal lines, which extend the traditional local goal from a single point to multiple candidate lines. Coupled with a topological map construction strategy that groups obstacles to be as convex as possible, a goal-adaptive navigation framework is proposed to efficiently plan multiple candidate trajectories. Simulations and experiments demonstrate that the proposed GA-TEB framework effectively prevents deadlock situations, where the robot becomes frozen due to a lack of feasible trajectories in crowded environments. Additionally, the framework greatly increases planning frequency in scenarios with numerous non-convex obstacles, enhancing both robustness and safety.

Figures (8)

• Figure 1: Issues of local goal in classic navigation framework.
• Figure 2: Illustration of obstacle group cluster with max-convex boundary.
• Figure 3: Illustration of group connections based on the group-level voronoi graph.
• Figure 4: Goal lines in (a) lobby with all three goal lines being retained and (b) corridor with one goal line being removed.
• Figure 5: Illustration of trajectory initialization and optimization.