No abstract

This research presents a new multi-objective path planning method for an unmanned aerial vehicle (UAV) using evolutionary computation. The proposed method searches for a desirable Pareto-optimal solution using an "aspiration point" and an "ideal point." The aspiration point refers to the preference information for a decision maker (DM), and the ideal point represents a virtual solution that optimizes all objective functions simultaneously. All of the solutions generated in using evolutionary computation evolve toward the aspiration region, which is determined by the aspiration point. If a solution that is closer to the ideal point than the aspiration point is generated in the search process, the aspiration point is moved to the position of the solution point. This process is repeated until specific termination conditions are satisfied. Some results of the benchmark test problems show that the proposed method can efficiently generate the Pareto-optimal solution for the DM and a high probability compared to the existing method called the "weighted-sum method." The usefulness of the proposed method is also shown by applying it to a multi-objective path planning problem that assumes an aerial photo-shoot mission using a UAV.

The purpose of our research is to formulate a drone delivery problem (DDP) as a constrained multi-objective optimization problem and evaluate the cost-reduction effect of a drone delivery service using the provisional-ideal-point (PIP) method proposed in this paper. The original PIP method is a genetic algorithm-based (GA-based) optimization method that can efficiently generate a preferred solution for a decision-maker. However, there are two problems occur when this method is applied to the DDP. The first problem is that there exist some cases wherein the evaluation function becomes infinite in the search process, making it impossible to sort the generated solutions. The second problem is that a long time is needed for the solution search to converge. Accordingly, the process had to be aborted at the halfway point. We present an improved PIP method to overcome these two problems. The proposed method is a solution search comprising a GA combined with tabu search. It converts the DDP into a single-objective optimization problem of a delivery cost using conversion factors. This paper presents several things understood regarding the cost-reduction effect on drone delivery services using our newly proposed method.