Vision Based Obstacle Avoidance for UAVs
This paper describes a vision based navigation and guidance design for UAVs for a combined mission of waypoint tracking and collision avoidance with unforeseen obstacles using a single 2 D passive vision sensor. An extended Kalman fllter (EKF) is applied to estimate a relative position of obstacles from vision based measurements. The stochastic z test value is used to solve a correspondence problem between the measurements and the estimates that have been already obtained by then. A collision cone approach is used as a collision criteria in order to examine if there is any obstacle that is critical to the vehicle. A guidance strategy for collision avoidance is designed based on a minimum efiort guidance (MEG) method for multiple target tracking. The vision based navigation and guidance designs suggested in this paper are integrated with realtime image processing algorithm and the entire vision based control system are evaluated in the closed loop 6 DoF ight simulation.