Generative real-time planning for multi-robot contingencies using deep prior-guided whale optimization.

Introduction: Dynamic multi-robot coordination demands real-time resilience against stochastic disruptions, yet existing planning methodologies often falter under the computational burden of high-dimensional state transitions. To address this challenge, we present a generative real-time mission planning framework that integrates deep prior learning with adaptive evolutionary optimization. Methods: By employing a subtraction-average-based optimizer (SABO) to tune long short-term memory (LSTM) networks, we extract deep spatiotemporal priors that map discrete contingency events to continuous cost intervals, effectively compressing the feasible search space. Within this refined subspace, a guided whale optimization algorithm (GWOA), which is augmented by target-gap adaptive control and dimension-wise opposition learning, executes precise plan regeneration. Results: Validation across six distinct contingency scenarios, ranging from risk-induced termination to agent paralysis, demonstrates that the proposed approach outperforms traditional reallocation baselines, achieving rapid responsiveness and superior solution stability. The framework also demonstrates scalability in large-scale scenarios involving up to 1000 agents. Discussion: These results demonstrate that integrating deep spatiotemporal prior learning with adaptive evolutionary optimization can effectively reduce the computational burden of mission replanning while maintaining responsiveness, stability, and scalability, providing a robust approach to resilient multi-robot management in unpredictable environments.