Multi-robot Target Search under Multi-peak Distribution: A Dynamic Approach based on High Confidence Area

Target search with multiple robots has attracted widespread attention for its numerous applications, $eg$., surveillance, reconnaissance and environmental exploration. In this paper, we propose a heuristic target search scheme for multi-robot, considering the prior information of targets is unreliable. To tactfully capture the multi-peak characteristics of the probability distribution map (PDM) of each target, we introduce the concept of high confidence area (HCA) based on the Gaussian mixture model. Then, a coordinated search method consisting of task allocation and path planning is designed to achieve efficient search performance. The main novelty of our method is twofold. First, the probability information of multi-peak is sufficiently captured by HCA and evaluated by reliability degree. Second, target allocation and path planning are designed coordinately, which dynamically update with real-time status and alleviate misleading effects even when PDM is not reliable, thereby largely reducing search time. Extensive contrastive simulations demonstrate that the HCA-based search method outperforms two other existing methods.