OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER

This dissertation presents the design and development of an obstacle detection and avoidance system for unmanned aerial vehicles, implemented by a neuro-fuzzy controller. In this context, this work presents a theoretical review of unmanned aerial vehicles, the applicable Brazilian legislation, obstacle detection methods, fuzzy logic and neural networks. The developed controller was implemented in order to mimic the actions taken by a human operator, aiming at avoiding obstacles found in the navigation path of the UAV. Inference rules were established based on consultation with specialists in the field and the weights adjusted by neural networks. The decisionmaking process takes into account information collected by a multichannel Lidar and ultrasonic sensors embedded in the UAV. In turn, the developed algorithm was embedded in a commercial flight controller. The complete quadricopter system is detailed, highlighting the key features of all sensors and the flight controller. The results of computational simulations and experimental tests are presented, discussed and compared, in order to evaluate the performance of the developed system.