Fast Expanding Safe Circular Regions for Efficient Local Path Planning

TL;DR

This paper introduces a geometric algorithm for local robot navigation that computes expanding circular regions from LiDAR data, enabling faster planning and longer horizons in complex environments.

Contribution

It presents a novel geometric approach for local path planning using expanding circular regions, improving speed and horizon length over existing methods.

Findings

Implemented in ROS2 and evaluated in simulation.

Achieved faster computation times and longer planning horizons.

Effective in complex local navigation scenarios.

Abstract

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.