Pattern-Aware Task Assignment and Depth-Based Deconfliction for Multi-AUV Oceanic Search Operations

The deployment of multiple Autonomous Underwater Vehicles (AUVs) for large-scale oceanic search and mapping missions is frequently constrained by inefficient task assignment and strict collision avoidance protocols. Traditional deconfliction methods block adjacent operational regions, significantly reducing the feasible search area and delaying the exploration of high-priority zones. Although this limitation is widely acknowledged in recent surveys, the direct integration of physical search-pattern geometry into the assignment constraints themselves remains largely unexplored. To address this gap, a mathematical model for dynamic task assignment executed across macro- and micro-cycles is proposed. The objective is formulated to maximize search rewards based on finding likelihood and temporal efficiency. To bypass the restrictive nature of neighboring-region exclusion constraints, a novel integration of alternating narrowing and widening spiral patterns is introduced, combined with depth-based layer assignment for transiting AUVs. The relaxation is validated at two levels. At the constraint level, systematic Monte Carlo simulations over 50 trials show that the pattern-aware method attains a statistically significant reward improvement over a standard strict-adjacency baseline (paired t(49)=7.15, p