This paper describes a robust multi-UAV route planning problem in which any one of the vehicles could fail during plan execution at any visited location. The UAVs must visit a set of fixed locations; if one UAV fails, the other vehicles must cover any unvisited locations. The objective is to optimize the worst-case cost. This paper formulates the problem with a min-sum objective (minimizing the total distance traveled by all vehicles) and a min-max objective (minimizing the maximum distance traveled by any vehicle). A Genetic Algorithm (GA) was used to find approximate robust optimal solutions on seven instances. The results show that the GA was able to find solutions that have better worst-case cost than the solutions generated by other approaches that were tested.
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