Study on UAV Path Planning Based on Bellman-Ford Algorithm
Zhu Fan · Journal of Projectiles.Rockets.Missiles and Guidance · 2007
An unmanned aerial vehicle(UAV) path planning method in uncertain and adversarial environment is proposed based on the prior surveillance and experiential evaluation.Bayes rule is used to get the probability of threats in the given planning area,the probability map is constructed and then used to generate a weighted diagram,and the link weight of the diagram is the threat probability of related cell in the probability map.Bellman-Ford algorithm is used to search a minimum risk path of UAV from the origin to the target in the weighted diagram.Based on the maneuverability constraints of UAV,the minimum risk path is optimized to an optimal path.Through the simulation results of the algorithm,the validity of this method is proved successfully.