Obstacle Avoidance System for a Quadrotor UAV.

The implementation of the S&C capability was based on the Rapidly-Exploring Random Tree (RRT) and optimal path algorithms. Both of these algorithms required that a map of the surroundings be known prior to navigating between two or more designated waypoints. The location of the quadrotor was determined using IMU data and quadrotor dynamics. The RRT algorithm makes use of a randomly growing space-filling tree that expands to cover unexplored regions of the environment. Each node in this tree is represented as an intermediate waypoint. The optimal path algorithm chooses the shortest route from the starting location and a target waypoint, which is manually specified in the code. The final result of the optimal path algorithm is shown for a sample case, where the quadrotor must navigate between two rooms that is connected by an open doorway. SYSTEM ARCHITECTURE & BEHAVIOR: The functional behavior of the individual components are shown in Figure 4 below. The autopilot provides IMU data which are used to obtain the quadrotor’s position relative to its starting location. This is calculated from the dynamics equations of the quadrotor. The quadrotor then computes the distance to the next waypoint, changes its heading, and flies there using the built-in PID control system. This process is continued until the target waypoint is reached. The sonars assist in altitude maintenance and in keeping the quadrotor at a safe distance from walls or obstacles. Figure 1. Quadrotor RESULTS & ANALYSIS: The algorithm and functionality of the quadrotor was tested as follows. The algorithm was regression tested to check for bugs. Meanwhile, the quadrotor was bench-tested to see its response to nearby obstacles. The algorithms were then implemented onto the autopilot and several flight tests were done in the hallways of the apartments at Cal Poly Pomona’s Village residential area. Overall, adequate results have not been obtained to gauge the success of the obstacle avoidance capability and testing is still ongoing. The following issues were detected. The quadrotor was found to drift in autonomous mode and could not maintain a steady position. This is due to the sensitivity of the sonar, as shown in Figure 5. Drifting was also due to the quadrotor’s propeller wash when in close proximity to the ground. In addition, the quadrotor position measurements were not accurate. The position was inaccurate by a few centimeters. This caused the quadrotor to miss its intermediate and target waypoints. Finally, the quadrotor only responded to static obstacles (i.e. obstacles that were already known to exist on the environment map). Unknown obstacles that were detected during navigation (pop-up obstacles) caused the quadrotor to stop and fail to pursue its target waypoint.