Observability-based local path planning and collision avoidance for micro air vehicles using bearing-only measurements

In this paper we detail an observability based path planning algorithm for Small and Miniature Air Vehicles (MAVs) navigating among multiple static obstacles. Bearing only measurements are utilized to estimate the time-to-collision (TTC) and bearing to obstacles using the extended Kalman filter (EKF). For the error covariance matrix computed by the EKF to be bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability. We use these conditions to design a path planning algorithm which simultaneously minimizes the uncertainties in state estimation while avoiding collisions with obstacles. Simulation results show that the planning algorithm successfully solves the single and multiple obstacle avoidance problems for MAVs while improving the estimation accuracy.