| Abstract: | An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV) swarm. This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously. At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results. Through iterations of this process,the cooperative search of UAV swarm for mission area is realized. The state transition rule is divided into two types. If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used. The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly. And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets. Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm. |
