Combined Optimal Control and Combinatorial Optimization for Searching and Tracking Using an Unmanned Aerial Vehicle
Journal article, Peer reviewed
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Combined searching and tracking of objects using Unmanned Aerial Vehicles (UAVs) is an important task with many applications. One way to approach this task is to formulate path-planning as a continuous optimal control problem. However, such formulations will, in general, be complex and difficult to solve with global optimality. Therefore, we propose a two-layer framework, in which the first layer uses a Traveling-Salesman-type formulation implemented using combinatorial optimization to find a near-globally-optimal path. This path is refined in the second layer using a continuous optimal control formulation that takes UAV dynamics and constraints into consideration. Searching and tracking problems usually trade-off, often in a manual or ad-hoc manner, between searching unexplored areas and keeping track of already known objects. Instead, we derive a result that enables prioritization between searching and tracking based on the probability of finding a new object weighted against the probability of losing tracked objects. Based on this result, we construct a new algorithm for searching and tracking. This algorithm is validated in simulation, where it is compared to multiple base cases as well as a case utilizing perfect knowledge of the positions of the objects. The simulations demonstrate that the algorithm performs significantly better than the base cases, with an improvement of approximately 5-15%, while it is approximately 20-25% worse than the perfect case.