Colloquium: Dequan Ou (C&O)

Insects like honeybees exhibit remarkable navigational abilities despite their simple nervous systems, showcasing expertise in tasks such as long-distance travel, landmark recognition, and spatial memory. These skills are crucial for efficient foraging and homing.  In robotics, one of the main challenges is to navigate in GPS-denied environments with limited sensors and processors onboard. In this study, we propose a novel navigation strategy that uses learning flights during which a robot directly maps images to nest location vectors, inspired by honeybees. In our previous research, the learning phase was modeled using compact convolutional neural networks (CNNs) and demonstrated successful learning and control in simulation.  In this work, we combine this homing model with odometry and implement both in a real-world quadrotor. More specifically, we examine how this model can compensate for the drone’s odometric drift to reach home after it performs a long-distance outbound flight. Real-world experiments demonstrate the proof of concept of the proposed navigation strategy in both indoor and outdoor flights.

Supervisor: Prof.dr. Guido C.H.E. de Croon