Multi-agent Search and Containment for the Mobile Target: An Information Map and Gaussian Belief Propagation-Based Approach

The search and containment problem requires a multi-agent system to alternate between dispersion for information acquisition and gathering for target capture under spatiotemporal constraints. Existing studies typically treat search and containment as distinct tasks, whereas this paper shows that they can be addressed within a unified endpoint decision formulation under stagedependent parameter settings. Specifically, this paper presents a unified framework that predicts target’s position via a hierarchical information map, and generates endpoints in discrete space using a convolution-based greedy algorithm. Then, Gaussian belief propagation is used to refine endpoints and trajectories in continuous space while enforcing safety constraints and stage-dependent termination conditions. Simulations across diverse maps demonstrate order-of-magnitude runtime reductions relative to an integer programming solver, while achieving competitive terminal time and a lower collision rate compared to a baseline that designs separate algorithms for search and containment.