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

Abstract

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. 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. 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. 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.