Distributed adaptive coalition task allocation based on reachable region online prediction for munition swarm

Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements. This paper proposes a reachable region guided distributed coalition formation game (RRG-DCF) method to address these issues. To enable efficient online task allocation, a reachable region prediction strategy based on fully connected neural networks (FCNNs) is developed. This strategy integrates high-fidelity data generated from the golden section method and low-fidelity data from geometric approximation in an optimal mixing ratio to form multi-fidelity samples, significantly enhancing prediction accuracy and efficiency under limited high-fidelity samples. These predictions are then incorporated into the coalition formation game framework. A tabu search mechanism guided by the reachable region center directs munitions to execute tasks within their respective reachable regions, mitigating redundant operations on ineffective coalition structures. Furthermore, an adaptive guidance coalition formation strategy optimizes allocation plans by leveraging the hit probabilities of munitions, replacing traditional random coalition formation methods. Simulation results demonstrate that RRG-DCF surpasses the contract network protocol and traditional coalition formation game algorithms in optimality and computational efficiency. Hardware experiments further validate the method's practicality in dynamic scenarios.