Spatiotemporal synchronization-aware cross-domain mission planning for air-sea-underwater heterogeneous unmanned swarms

Abstract

Facing the increasing complexity of multi-dimensional maritime operations, cross-domain heterogeneous unmanned swarms provide an appealing paradigm for comprehensive ocean perception. However, platform disparities and strict spatiotemporal synchronization requirements challenge mission planning where task assignment and path planning are tightly coupled. To address this, we design a single-stage cooperative planning framework tailored for air-sea-underwater swarms. First, a recursive temporal deduction mechanism is introduced to accurately quantify the cooperative waiting costs induced by platform heterogeneity. Subsequently, a Q-learning enhanced Genetic Algorithm integrating chaotic initialization and Opposition-Based Learning, named COGAQ, is proposed for rapid solution. This algorithm adopts a feasible coalition set index encoding strategy to limit the search space and integrates variable neighborhood search to rectify temporal asynchrony. Comparative experiments against mainstream algorithms validate the effectiveness of the designed framework, highlighting the optimum-seeking capability of COGAQ in handling tightly coupled constraints. Furthermore, a large-scale scenario containing 55 heterogeneous platforms and 102 targets verifies the algorithm's scalability. This paper provides a precise and efficient solution for air-sea-underwater heterogeneous temporal coordination problems, presenting an appealing approach for cooperative mission planning in complex scenarios like maritime search and rescue, ocean resource monitoring, and joint anti-submarine operations.