Occlusionaware UAV Path Planning for Reconnaissance and Surveillance in Complex Environments This paper concerns the reconnaissance and surveillance problem for multirotor aerial drones or unmanned aerial vehicles (UAVs) flying over geometrically complex environments, such as mountainous terrains and urban regions. In contrast to the existing literature, our approach takes both UAV kinematics constraints and camera sensing constraints into consideration. We present a twostage strategy to solve this UAV surveillance problem with the given terrain information. In the first stage, the challenge is to find a set of camera locations called the vantage waypoint set to provide full coverage of the area of interest, which can be viewed as a 3D Art Gallery Problem using drones as the observers. In the second stage, one or several UAVs are determined to conduct the full coverage reconnaissance and surveillance duty along individual routes respecting their kinematic constraints in the optimization criterion (the shortest time possible). This variant of the combinatorial traveling salesman problem is solved by introducing unsupervised learning and Bezier curves. The reported results support the feasibility of the proposed solution.

Constrained Heterogeneous Vehicle Path Planning for Largearea Coverage There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVsxe2x80x99 battery life and communication distance, complete coverage of large areas typically involves multiple takeoffs and landings to recharge batteries, and the transportation of UAVs between operation areas by a ground vehicle. In this paper, we introduce a novel largeareacoverage planning framework which collectively optimizes the paths for aerial and ground vehicles. Our method first partitions a large area into subareas, each of which a given fleet of UAVs can cover without recharging batteries. UAV operation routes, or trails, are then generated for each subarea. Next, the assignment of trials to different UAVs and the order in which UAVs visit their assigned trails are simultaneously optimized to minimize the total UAV flight distance. Finally, a ground vehicle transportation path which visits all subareas is found by solving an asymmetric traveling salesman problem (ATSP). Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. We show that the solution time can be reduced to closetorealtime levels by first finding a feasible solution using a Random Key Genetic Algorithm (RKGA), which is then locally optimized by solving a much smaller MIQP.