Search

In this paper, the problem of cooperative search and tracking by multiple flying vehicles is studied. An algorithm is proposed based on optimal planning by minimizing a suitably defined cost function. In order to define the cost function, three maps are proposed: uncertainty map, detection probability map, and emitter map. In the uncertainty map, the geometrical status of the vehicle with respect to the region is considered and its suitability is measured. The detection probability map is defined such that the group of vehicles can detect new emitters that appear in their sensors’ coverage. Finally, the emitter map is a tool to keep tracking the detected emitters with acceptable error while searching the region. The assumption that the vehicles are equipped with only one commonly used radar range indicator sensor makes the proposed algorithms more applicable in the real world for a cost-effective system. The efficiency of the proposed method from the search, detection, and tracking points of view is verified through simulations.

The focus of this paper is the use of Unmanned Aerial Vehicles (UAVs) for searching multiple targets under uncertain conditions in the minimal possible time. The problem, known as Minimum Time Search (MTS), belongs to the Probabilistic Search (PS) field and addresses critical missions, such as search & rescue, and military surveillance. These operations, characterized by complex and uncertain environments, demand efficient UAV trajectory optimization. The multi-target version of PS introduces additional challenges, due to their higher complexity and the need to wisely distribute the UAV's efforts among multiple targets. In order to tackle the under-explored multi-target aspect of MTS, we optimize the time to find all targets with new Ant Colony Optimization (ACO)-based planner. This novel optimization criterion is formulated using Bayes' theory, considering probability models of the targets (initial belief and motion model) and the sensor likelihood. Our work contributes significantly by (i) developing an objective function tailored for multi-target MTS, (ii) proposing an ACO-based planner designed to effectively handle the complexities of multiple moving targets, and (iii) introducing a novel constructive heuristic that is used by the ACO-based planner, specifically designed for the multi-target MTS problem. The efficacy of our approach is demonstrated through comprehensive analysis and validation across various scenarios, showing superior performance over existing methods in complex multi-target MTS problems. • Efficient optimization of drone trajectories in multi-target search missions. • Expected time of finding the targets as a key criterion in multi-target detection. • New multi-target heuristic enhances the performance of minimum time search planners. • Proposed Ant Colony-based planner optimizes search paths in complex missions. [ABSTRACT FROM AUTHOR]