Key-Scan-Based Mobile Robot Navigation: Integrated Mapping, Planning, and Control using Graphs of Scan Regions

TL;DR

This paper introduces a novel key-scan-based navigation framework for mobile robots that uses star-convex scan regions in pose graphs to enable safe, efficient, and integrated mapping, planning, and control in unknown cluttered environments.

Contribution

It proposes a new pose graph of star-convex scan regions with local navigation policies and a perception-driven planning method, advancing autonomous navigation in unstructured environments.

Findings

Effective navigation demonstrated in simulations and real experiments.

Proven safety and correctness of the local scan navigation policies.

Enhanced mapping and exploration capabilities through key scan selection.

Abstract

Safe autonomous navigation in a priori unknown environments is an essential skill for mobile robots to reliably and adaptively perform diverse tasks (e.g., delivery, inspection, and interaction) in unstructured cluttered environments. Hybrid metric-topological maps, constructed as a pose graph of local submaps, offer a computationally efficient world representation for adaptive mapping, planning, and control at the regional level. In this paper, we consider a pose graph of locally sensed star-convex scan regions as a metric-topological map, with star convexity enabling simple yet effective local navigation strategies. We design a new family of safe local scan navigation policies and present a perception-driven feedback motion planning method through the sequential composition of local scan navigation policies, enabling provably correct and safe robot navigation over the union of local scan regions. We introduce a new concept of bridging and frontier scans for automated key scan selection and exploration for integrated mapping and navigation in unknown environments. We demonstrate the effectiveness of our key-scan-based navigation and mapping framework using a mobile robot equipped with a 360$^{\circ}$ laser range scanner in 2D cluttered environments through numerical ROS-Gazebo simulations and real hardware~experiments.