Autonomous navigation of quadrupeds using coverage path planning with morphological skeleton maps

TL;DR

This paper introduces a new method for quadruped robots to autonomously navigate and scan unstructured environments using morphological skeleton maps and path planning.

Contribution

The novel use of morphological skeleton maps for coverage path planning in quadruped navigation is introduced.

Findings

The method achieved 86.5% waypoint reachability in five trials.

The map reader and path planner processed maps in 2.52 ms and 1.7 ms, respectively.

Drift in the 2D navigation map was identified as a limitation of the method.

Abstract

This article proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of a prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path based on the robot’s current position. To control the high-level operation, a finite state machine (FSM) is used to switch between two modes: navigating toward a POI using Nav2 and scanning the local surroundings. We validate the method in a leveled, indoor, obstacle-free, non-convex environment, evaluating time efficiency and reachability over five trials. The map reader and path planner can quickly process maps of widths and heights ranging between [196,225]
pixels
and [185,231]
pixels
in
2.52 ms
and
1.7 ms
, respectively. Their computation time increases with
22.0 ns/pixel
and 8.17 μs/pixel, respectively. The robot managed to reach 86.5% of all waypoints across the five runs. The proposed method suffers from drift occurring in the 2D navigation map.

Figures