Aerial robot swarms have the potential to perform time-critical and dangerous tasks such as disaster response without compromising human safety. However, their reliance on external infrastructure such as global positioning for localization and wireless networks for communication is still a limiting factor for many applications. Such infrastructure may not be available everywhere, increasing their chance of collisions in case of signal interruptions and limiting their robustness to failure. Moreover, agent-to-agent wireless communication can suffer from time delays and outages, preventing their scalability to large swarms that fly in dense configurations. Drones should have the autonomy to make their own decisions exclusively based on local sensory information to avoid scalability and robustness issues.
To address these limitations, we propose an entirely vision-based approach to swarm control inspired by flocking birds. In particular, we develop methods that enable drones to coordinate their motion by recognizing each other only with visual perception. We propose two distinct strategies that leverage the predictive power of convolutional neural networks: an end-to-end approach based on imitation learning and a modular system based on object detection and tracking. We test the algorithms using agent-based models and physics-based simulations with realistic sensor noise and validate them with a fleet of custom-built quadcopters in controlled indoor and challenging outdoor environments. The drones are equipped with an omnidirectional camera setup to avoid blind spots and onboard computation to process the images in real-time without requiring specialized hardware such as easy-to-recognize visual markers. Extensive simulations and real-world experiments show that vision-based swarms can perform collision-free and cohesive navigation while only relying on local visual information for control. We finally address the scalability of vision-based swarms in terms of group size and density.