Abstract
This paper focuses on planning the optimal paths of multiple unmanned aerial vehicles (UAVs) for searching a marine target. First, the marine target search mission is modeled in detail by formulating the target probability map, the UAV point-mass model, the sensor detection model, etc. Then, the consensus theory with state predictor is adopted to fuse the updated target probability maps, which UAVs preserve independently. With the simultaneous consideration of the local searching reward and the future searching reward, the motions or paths of UAVs are then optimized in real time by the distributed model predictive control (DMPC). Finally, the simulation results are given to verify the high searching efficiency of our proposed method.
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