  Collision avoidance algorithms are of central interest to many drone
applications. In particular, decentralized approaches may be the key to
enabling robust drone swarm solutions in cases where centralized communication
becomes computationally prohibitive. In this work, we draw biological
inspiration from flocks of starlings (Sturnus vulgaris) and apply the insight
to end-to-end learned decentralized collision avoidance. More specifically, we
propose a new, scalable observation model following a biomimetic
nearest-neighbor information constraint that leads to fast learning and good
collision avoidance behavior. By proposing a general reinforcement learning
approach, we obtain an end-to-end learning-based approach to integrating
collision avoidance with arbitrary tasks such as package collection and
formation change. To validate the generality of this approach, we successfully
apply our methodology through motion models of medium complexity, modeling
momentum and nonetheless allowing direct application to real world quadrotors
in conjunction with a standard PID controller. In contrast to prior works, we
find that in our sufficiently rich motion model, nearest-neighbor information
is indeed enough to learn effective collision avoidance behavior. Our learned
policies are tested in simulation and subsequently transferred to real-world
drones to validate their real-world applicability.
