This paper presents a vision-based collision avoidance technique for small and Miniature Air Vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained.
Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman Filter (EKF) that takes into account the correlations between obstacles.
We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the Rapidly-Exploring Random Tree (RRT) algorithm.
The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.
Source: Brigham Young University
Author: Huili Yu | Randy Beard
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