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

Dharshan Bashkaran Latha, Ömür Arslan

TL;DR

This work tackles safe navigation in unknown cluttered environments by tightly integrating mapping, planning, and control through a pose graph of locally sensed scan regions. It introduces a motion-graph framework built from star-convex scan polygons and local feedback policies, enabling provably correct global navigation via sequential policy composition. Key innovations include bridging and frontier scans for automatic key-scan selection to improve connectivity and active exploration, along with a perception-driven planning loop validated on a 2D robot with a 360° LIDAR in ROS-Gazebo simulations and real hardware. The results demonstrate improved computational efficiency, exploration coverage, and navigation safety in unknown environments, highlighting the benefits of action-driven perception for robust autonomous systems.

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.

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

TL;DR

This work tackles safe navigation in unknown cluttered environments by tightly integrating mapping, planning, and control through a pose graph of locally sensed scan regions. It introduces a motion-graph framework built from star-convex scan polygons and local feedback policies, enabling provably correct global navigation via sequential policy composition. Key innovations include bridging and frontier scans for automatic key-scan selection to improve connectivity and active exploration, along with a perception-driven planning loop validated on a 2D robot with a 360° LIDAR in ROS-Gazebo simulations and real hardware. The results demonstrate improved computational efficiency, exploration coverage, and navigation safety in unknown environments, highlighting the benefits of action-driven perception for robust autonomous systems.

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 laser range scanner in 2D cluttered environments through numerical ROS-Gazebo simulations and real hardware~experiments.
Paper Structure (7 sections, 2 equations, 1 figure)

This paper contains 7 sections, 2 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Motivation and Related Literature
  3. Integrated Robot Motion Planing and Control
  4. Integrated Robot Perception and Action
  5. Contributions and Organization of the Paper
  6. Safe Local Navigation over a Scan Region
  7. Robot Motion and Perception Model

Figures (1)

  • Figure 1: Key-scan-based mobile robot navigation in an unknown cluttered environment by online sequential deployment and composition of local scan navigation policies. (top-left) Automated key scan selection and deployment based on frontier and bridging scans. (top-middle) An incrementally built motion graph of star-convex scan polygons. (top-right) Global feedback motion planning via sequential composition of local scan navigation policies. (bottom) Example robot trajectories during autonomous exploration.