Research

Autonomous uncrewed areal vehicles require the ability to navigate various environments without collision failures. These systems already serve essential roles in various fields, from entertainment to military applications. There is a desire to replace costly multi-sensor-based designs with a system based solely on computer vision. However, these systems suffer from varying success rates in object recognition and obstacle avoidance. In some cases, object recognition is too slow to avoid collision failure. Current state-of-the-art solutions for computer vision-based systems implement artificial neural networks or an algorithm written specifically for a particular task. This work proposes a paired convolutional neural network architecture for the execution of object recognition and obstacle avoidance in uncrewed areal vehicles. This work anticipates the paired convolutional neural network architecture to produce a vision-based autonomous system with high success rates in obstacle avoidance and a low rate of collision failures while maintaining competitive flight speed.