Multi-objective Multiple Quadcopter Path Planning in Urban City

Applications using multiple quadcopters such as environment detection or packet delivery have drawn lots of interest from commercial companies. As the battery life of a quadcopter is very limited, a path planning algorithm can help to improve the efficiency of each flight. To avoid overloading some quadcopters while under-utilizing the others, the algorithm will minimize the total path lengths and balance individual path length. This problem is formulated as multi-objective multiple traveling salesman problem (MOMTSP). To generate flight paths quickly for commercial application, City Quadcopter Path Planner (CQPP) which is based on Non Sorting Genetic Algorithm II (NSGA-II) is proposed and applied to search for the solutions. Positive results are obtained from three benchmark scenarios, which are designed for testing the performance of the algorithm in solving this path planning problem.