Multi-UAV Path Planning with Multi Colony Ant Optimization

In the last few decades, Unmanned Aerial Vehicles (UAVs) have been widely used in different type of domains such as search and rescue missions, firefighting, farming, etc. To increase the efficiency and decrease the mission completion time, in most of these areas swarm UAVs, which consist of a team of UAVs, are preferred instead of using a single large UAV due to the decreasing the total cost and increasing the reliability of the whole system. One of the important research topics for the UAVs autonomous control system is the optimization of flight path planning, especially in complex environments. Lots of researchers get help from the evolutionary algorithms and/or swarm algorithms. However, due to the increased complexity of the problem with more control points which need to be checked and mission requirements, some additional mechanisms such as parallel programming and/or multi-core computing is needed to decrease the calculation time. In this paper, to solve the path planning problem of multi-UAVs, an enhanced version of Ant Colony Optimization (ACO) algorithm, named as multi-colony ant optimization, is proposed. To increase the speed of computing, the proposed algorithm is implemented on a parallel computing platform: CUDA. The experimental results show the efficiency and the effectiveness of the proposed approach under different scenarios.