Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios

TL;DR

This paper introduces a comprehensive benchmark for classical coverage path planning heuristics on irregular hexagonal grids, focusing on maritime scenarios, and evaluates their performance across a large dataset.

Contribution

It provides a reproducible benchmark with 10,000 instances, evaluating 17 heuristics, and highlights the impact of implementation details on heuristic performance.

Findings

Heuristics with explicit shortest-path reconnection are reliable but rarely produce zero-revisit tours.

Every instance in the dataset is confirmed to be Hamiltonian-feasible.

A Warnsdorff variant achieves 79.0% Hamiltonian success, influenced by residual degree definitions.

Abstract

Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical methods are often compared on small ad hoc examples or on rectangular grids. This paper presents a reproducible benchmark of deterministic single-vehicle coverage path planning heuristics on irregular hexagonal graphs derived from synthetic but maritime-motivated areas of interest. The benchmark contains 10,000 Hamiltonian-feasible instances spanning compact, elongated, and irregular morphologies, 17 heuristics from seven families, and a common evaluation protocol covering Hamiltonian success, complete-coverage success, revisits, path length, heading changes, and CPU latency. Across the released dataset, heuristics with explicit shortest-path reconnection solve the relaxed coverage task reliably but almost never produce zero-revisit tours. Exact Depth-First Search confirms that every released instance is Hamiltonian-feasible. The strongest classical Hamiltonian baseline is a Warnsdorff variant that uses an index-based tie-break together with a terminal-inclusive residual-degree policy, reaching 79.0% Hamiltonian success. The dominant design choice is not tie-breaking alone, but how the residual degree is defined when the endpoint is reserved until the final move. This shows that underreported implementation details can materially affect performance on sparse geometric graphs with bottlenecks. The benchmark is intended as a controlled testbed for heuristic analysis rather than as a claim of operational optimality at fleet scale.