Energy-optimal path planning for six-rotors on multi-target missions

Often times in multi-target, Unmanned Aerial System (UAS) applications, minimizing energy consumption is a common goal. In simplified cases, this problem can be solved by minimizing the path length between targets using the Traveling Salesman Problem with Neighborhoods (TSPN). However, this method does not take into account the attitude of the UAS. In addition, minimum path length does not always correlate to minimum energy consumption. This research proposes an approach to both problems. A method is proposed to more accurately model multi-target, six-rotor aircraft missions using the Generalized TSPN (GTSPN), which can account for the non-convex characteristics of the neighborhoods created by accounting for variable attitude. To address the energy consumption problem, a heuristic procedure is proposed to solve GTSPN instances using a genetic algorithm approach that incorporates a fine-tuned, dynamic model of a six-rotor to simulate energy consumption. Numerical simulations are performed on randomly generated instances with up to 50 targets to show that the proposed procedure determines tours for a given instance within a 1% standard deviation error from the best-known tour and on average improves the results obtained with the TSPN formulation up to 11.4%.