# 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.

## Key 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.