Distributed Vision-Only Cooperative Flight of Multiple Quadrotors in Unknown Cramped Environments

Multi-quadrotor systems hold substantial promise for various applications, yet navigating autonomously in complex and unknown environments like forests and alleyways remains a formidable challenge. Contemporary multi-UAV systems predominantly rely on wireless communication for navigation in environments with known or no obstacles. Further extensive research is imperative to enhance the perception, cooperation, and control of multi-UAV systems. This paper delineates the development of a distributed vision-only multi-UAV system, wherein the agents exclusively depend on onboard sensing and computation for navigation and cooperative flight in unknown, constrained environments. The onboard sensors comprise a front-depth camera and an omnidirectional gray monocular camera. The end-to-end sensorimotor controller computes the control commands, effectuating a direct mapping from perception to action through a sophisticated neural network design. This design is trained in simulation via imitation learning, namely, by emulating an expert endowed with access to privileged information. Such a setup empowers agents to accomplish cooperative formation flight, obstacle avoidance, target tracking, and other maneuvers in constrained environments, independent of wireless communication. Our findings underscore that distributed vision-only policies can rival traditional and other learning-based autonomous flight methodologies in challenging environments.