Unmanned aerial vehicle detection path optimization of ship exhaust emissions considering real-time location of ships
To improve the efficiency of unmanned aerial vehi-cle(UAV)detection of ship exhaust emissions,the path planning problem of UAV when both UAV and ship were mov-ing simultaneously has been investigated.Aiming at the real-time changes of the ships position during the detection process,the"meeting"model was adopted to solve the en-counter position of ships and UAV,and a UAV path optimiza-tion model targeting the minimum flight distance of moving ships was established to achieve collaborative detection of multiple UAVs accordingly.The two-stage algorithm by combi-ning sequence insertion algorithm and a genetic algorithm based on two ship sequence decomposition strategies,DroneByDrone and ShipByShip were designed to achieve UAV path optimization in scenarios of different scales.Numerical experiments show that a path optimization method that taking into account of both UAV flight and ship navigation can effec-tively improve the detection efficiency of moving ships.Both algorithms can successfully solve the model,but compared to the two-stage algorithm,the genetic algorithm can shorten the solving time by 65.12％,meeting the requirements of solving timeliness.The flight distance of UAVs is significantly sensi-tive to the number of UAVs,in the same dataset,dispatching two UAVs can optimize the flight distance by 25％compared to three UAVs,but the detection time will be correspondingly delayed by 8％.By comprehensively optimizing the position of UAV base stations,the number and speed of UAVs based on experimental scenarios,the flight distance of UAVs can be ef-fectively shortened,and the detection efficiency can be im-proved.
万方数据
维普
