Noise-Optimal Arrival Route Planning for Operations of Multiple Unmanned Aerial Vehicles

The noise generated by aerial vehicles during the departure and landing procedures is one of the major concerns for communities living in the vicinity of airports. In order to mitigate such negative impacts, in this paper, we consider to optimize the arrival routes for multiple unmanned aerial vehicles (UAVs) simultaneously, so that the minimum level of noise will be generated during the landing procedure. As a prerequisite to our UAV route planning framework, the noise evaluation model is first introduced for each possible path of the UAVs. A mixed-integer linear program (MILP) is then formulated for solving the noise-optimal arrival route for each of the UAVs and meanwhile ensure the collision avoidance among all of them. Considering that the computational complexity of classical brunch-and-bound method grows exponentially as the number of UAVs increases, we present a time-expansion network based approach whose solution time is primarily reduced and thus can be implemented in real-time. To further enhance the solution quality, we impose various velocity options into the planning model and show that complexity of the programming problem increases linearly by leveraging the sparsity of matrices. Finally, a case study is conducted to validate the effectiveness of our noise-optimal route planning framework.