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This study proposes a novel multi-objective integer programming model for acollision-free discrete drone path planning problem. Considering thepossibility of bypassing obstacles or flying above them, this study aims tominimize the path length, energy consumption, and maximum path risksimultaneously. The static environment is represented as 3D grid cells. Due tothe NP-hardness nature of the problem, several state-of-theart evolutionarymulti-objective optimization (EMO) algorithms with customized crossover andmutation operators are applied to find a set of non-dominated solutions. Theresults show the effectiveness of applied algorithms in solving severalgenerated test cases.

Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem