Distributed Cooperative Search Algorithm with Information Screening

This paper focuses on distributed cooperative search using multiple Unmanned Ground Vehicles (UGVs) for a dynamic target. To enhance search efficiency and optimize resource usage, we propose improvements in the search algorithm framework, including advancements in search map creation and updates, distributed information fusion, and collaborative decision-making. In contrast to traditional approaches, we introduce environmental uncertainty, which correlates with cell detection intervals, reflecting information importance. In the information fusion stage, our innovative mechanism screens information based on decision horizon and environmental uncertainty, while considering communication constraints to minimize redundant data transmission. Additionally, in the collaborative decision stage, the utility function was optimized by considering the cumulative environmental uncertainties for generated path covering cells, thereby enhancing search area coverage and cell revisit probability. Experimental results with four UGVs demonstrate the superiority of our algorithm, achieving 11.7% search efficiency improvement, 11.8% trajectory length reduction and 45.6% computation time reduction compared with data fusion algorithm, validating our approach’s effectiveness in practical scenarios.