Decentralized Swarm Collision Avoidance for Quadrotors via End-to-End Reinforcement Learning

• URL: http://arxiv.org/abs/2104.14912v1
• Date: Fri, 30 Apr 2021 11:19:03 GMT
• Title: Decentralized Swarm Collision Avoidance for Quadrotors via End-to-End Reinforcement Learning
• Authors: Ramzi Ourari, Kai Cui, Heinz Koeppl
• Abstract summary: We draw biological inspiration from flocks of starlings and apply the insight to end-to-end learned decentralized collision avoidance. We propose a new, scalable observation model following a biomimetic topological interaction rule. Our learned policies are tested in simulation and subsequently transferred to real-world drones to validate their real-world applicability.
• Score: 28.592704336574158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 topological interaction rule that leads to stable learning and robust avoidance behavior. Additionally, prior work primarily focuses on invoking a separation principle, i.e. designing collision avoidance independent of specific tasks. By applying a general reinforcement learning approach, we propose a holistic learning-based approach to integrating collision avoidance with various tasks and dynamics. To validate the generality of this approach, we successfully apply our methodology to a number of configurations. Our learned policies are tested in simulation and subsequently transferred to real-world drones to validate their real-world applicability.

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