Study Of Improved Pilot Performance Using Automatic Collision Avoidance For Tele-Operated Unmanned Aerial Vehicles

This paper studies the application of automatic collision avoidance algorithms to help pilots improve their maneuvering of unmanned aerial vehicles (UAVs). Automatic collision avoidance technology can help reduce the cognitive workload of a pilot, especially when Hying UAVs through cluttered and complex unstructured environments. The feedforward-based algorithm reviewed herein exploits the dynamics of the aerial robot and if a collision is predicted, the algorithm modifies the operator's input to avoid a collision. The algorithm has recently been implemented on a quadcopter UAV with on-board computation and sensing. To quantify the improvement in pilot performance compared to other methods, human-subject studies were conducted using a simulated quadcopter UAV running the collision avoidance algorithms. Specifically, a comparison is made between the feedforward-based algorithm, the basic risk field algorithm (a variant on potential field), and full manual control. Experimental results show that the feedforward-based algorithm performs significantly better than manual control by lowering the number of collisions and increasing the UAV's average speed, both of which are extremely vital, for example, for UAV-assisted search-and-rescue applications. Compared to the potential-field based algorithm, the feedforward algorithm enabled the pilot to operate the UAV with significantly higher average speeds without drastically affecting the number of collisions.