Comparison of Innovative Strategies for the Coverage Problem: Path Planning, Search Optimization, and Applications in Underwater Robotics

TL;DR

This paper compares different coverage path planning strategies for underwater robotics, evaluating their efficiency, computational costs, and suitability for various mission constraints through simulations.

Contribution

It introduces a comparative analysis of TSP, MST, and OCP approaches for underwater coverage, highlighting their trade-offs and practical implications.

Findings

OCP is optimal for time-constrained missions but computationally intensive.

MST approaches are faster but less optimal in coverage.

Results guide algorithm selection based on mission priorities.

Abstract

In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path planning strategies to enhance the efficiency of underwater gliders, particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Optimal Control Problem (OCP). Simulations were conducted in MATLAB, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but less optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasibility.