用目标线替代点，让机器人在拥挤中更安全高效地导航
GA-TEB: Goal-Adaptive Framework for Efficient Navigation Based on Goal Lines
- 将局部目标从点扩展为多条候选目标线，适应远距离全局目标
- 在密集人群环境中显著减少死锁，规划频率提升明显
- 适合复杂障碍场景下的移动机器人导航，尤其非凸障碍多时
在人群导航中，局部目标对轨迹初始化、优化和评估至关重要。考虑到当全局目标较远时，机器人的首要任务是避障而非精确通过局部目标点，本文提出目标线概念，将传统单一点状局部目标扩展为多条候选目标线。结合一种使障碍物分组尽可能凸化的拓扑地图构建策略，提出一种目标自适应导航框架，用于高效规划多条候选轨迹。仿真与实验表明，所提出的GA-TEB框架能有效防止机器人在拥挤环境中因无可行轨迹而陷入死锁状态。此外，在存在大量非凸障碍的场景中，该框架显著提高了规划频率，增强了系统的鲁棒性与安全性。
原文摘要 · Abstract (English)
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.
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