A Quantum Computing Approach for Multi-robot Coverage Path Planning

TL;DR

This paper introduces a quantum computing-based heuristic approach for multi-vehicle coverage path planning, aiming to improve efficiency in solving NP-hard problems like search and rescue or environmental monitoring.

Contribution

It presents a novel QAOA-compatible objective function, theoretical validation, and implementation strategies for multi-vehicle CPP, advancing quantum heuristic applications in path planning.

Findings

QAOA-based approach effectively solves multi-vehicle CPP

Theoretical proofs confirm approach validity

Performance surpasses classical algorithms like DFS

Abstract

This paper tackles the multi-vehicle Coverage Path Planning (CPP) problem, crucial for applications like search and rescue or environmental monitoring. Due to its NP-hard nature, finding optimal solutions becomes infeasible with larger problem sizes. This motivates the development of heuristic approaches that enhance efficiency even marginally. We propose a novel approach for exploring paths in a 2D grid, specifically designed for easy integration with the Quantum Alternating Operator Ansatz (QAOA), a powerful quantum heuristic. Our contribution includes: 1) An objective function tailored to solve the multi-vehicle CPP using QAOA. 2) Theoretical proofs guaranteeing the validity of the proposed approach. 3) Efficient construction of QAOA operators for practical implementation. 4) Resource estimation to assess the feasibility of QAOA execution. 5) Performance comparison against established algorithms like the Depth First Search. This work paves the way for leveraging quantum computing in optimizing multi-vehicle path planning, potentially leading to real-world advancements in various applications.