Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm that is able to consider sensor characteristics, the uncertainty of target detection, and complexity of needed maneuvers in order to make a multiagent search autonomous. This article provides a methodology for an autonomous 2-D search using multiple unmanned (aerial or possibly other) vehicles. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via a heat equation-driven area coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents that are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides a near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve the proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of an HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.

This paper considers a problem of area coverage where the objective is to achieve given coverage density by use of multiple mobile agents. We present an ergodicity-based coverage algorithm which enables a centralized feedback control for multiagent system based on radial basis function (RBF) representation of the ergodicity problem and a solution of an appropriately designed stationary heat equation for the potential field. The heat equation uses a source term that depends on the difference between the given goal density distribution and the current coverage density (time average of RBFs along trajectories). The agent movement is directed using the gradient of that potential field. The heat equation driven area coverage has a built-in cooperative behavior of agents which includes collision avoidance and coverage coordination. The algorithm is robust, scalable, and computationally inexpensive.