Fast Expanding Safe Circular Regions for Efficient Local Path Planning
For mobile robots requiring real-time local navigation, this method offers a computationally efficient alternative to existing approaches, though it is incremental and domain-specific.
The paper proposes a geometric algorithm for local robot navigation that computes expanding circular regions from LiDAR scans, achieving faster computation and longer planning horizons than optimization or learning-based methods. Evaluated in simulation, it demonstrates improved efficiency in complex environments.
Local navigation is one of the fundamental problems in robot navigation, and numerous approaches have been proposed over the years, including methods such as the Dynamic Window Approach, Model Predictive Control, and more recently, Control Barrier Functions and machine learning based techniques. While these methods perform well in simple environments, many of them rely on optimization or learning based procedures that can struggle in more complex scenarios. In contrast, this article proposes a more geometric algorithmic approach that enables a local navigation method with faster computation times and longer planning horizons. The proposed method is based on the computation of a sequence of circular regions from a local LiDAR scan that expand in the direction of the goal and capture free local navigable space. The proposed method was implemented in the ROS2 framework and evaluated in a simulated environment.