Cooperative Search Method for Surface Robot Swarms Based on Target Existence Probability and Coverage Information

Objectives To address the problems of insufficient adaptability of region-partitioning strategies under node failures and the tendency of probability-driven search methods to cause redundant coverage and local clustering in cooperative search of underwater moving targets by surface robot swarms, a cooperative receding-horizon search strategy is designed to account for both target probability cues and balanced coverage.Methods A target existence probability map and a coverage information model are constructed. The motion uncertainty of the underwater moving target is represented by probability diffusion, and the target existence probability is updated by integrating cumulative detection probability (CDP) decisions with Bayesian inference. Within a receding-horizon optimization framework, a coverage reward, future trajectory exchange, and field-of-view overlap penalty are introduced; model predictive control and particle swarm optimization are then employed to solve for the robots' motion control sequences. Results Monte Carlo simulations with 500 trials demonstrate that the proposed method achieves a target detection probability of 79.8%, with an average discovery time of 34.98 min for successful trials. Compared with the multi-surface-robot partition-based cooperative search method, the proposed method improves the target detection probability by 4.0% and reduces the average discovery time by 4.32%. Under the condition of single-robot node failure, the proposed method improves the target detection probability by 7.4% and reduces the average discovery time by 6.82%. Ablation studies indicate that removing the coverage gain term and the future overlap penalty term decreases the target detection probability by 5.4% and 2.4%, respectively.Conclusions The proposed method enables adaptive cooperative task allocation among multiple robots without fixed partition constraints, reduces redundant searching, and improves the efficiency of underwater moving target search and robustness against single-robot failures.