Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem
This addresses path planning for drones in static 3D environments, but it is incremental as it applies existing optimization methods with customizations.
The study tackled a multi-objective drone path planning problem by minimizing path length, energy consumption, and risk, using evolutionary algorithms to find non-dominated solutions in generated test cases.
This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and maximum path risk simultaneously. The static environment is represented as 3D grid cells. Due to the NP-hardness nature of the problem, several state-of-theart evolutionary multi-objective optimization (EMO) algorithms with customized crossover and mutation operators are applied to find a set of non-dominated solutions. The results show the effectiveness of applied algorithms in solving several generated test cases.