This paper proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of the 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 given the robot's current position. To control the high-level operation, a finite state machine is used to switch between two modes: navigating towards a POI using Nav2, and scanning the local surrounding. We validate the method in a leveled indoor obstacle-free non-convex environment on time efficiency and reachability over five trials. The map reader and the path planner can quickly process maps of width and height ranging between [196,225] pixels and [185,231] pixels in 2.52 ms/pixel and 1.7 ms/pixel, respectively, where 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 over all five runs. The proposed method suffers from drift occurring in the 2D navigation map.
@article{arxiv.2504.17880,
title = {Autonomous Navigation of Quadrupeds Using Coverage Path Planning with Morphological Skeleton Map},
author = {Alexander James Becoy and Kseniia Khomenko and Luka Peternel and Raj Thilak Rajan},
journal= {arXiv preprint arXiv:2504.17880},
year = {2026}
}
Comments
15 pages, published to Fronters In Robotics (currently in production), major revision: title change, abstract revised, grammar fixed, mathematical notations fixed and made consistent, conclusion revised, related works extended, Algorithm 1-3 revised