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.
翻译:避免碰撞的算法是许多无人机应用的核心利益。 特别是,在中央通信在计算上变得令人望而却步的情况下,分散的方法可能是使强健的无人机群群解解决办法的关键。 在这项工作中,我们从星群(Sturnus brugiis)中汲取生物灵感,并运用这种洞察力来避免从端到端的分散碰撞。更具体地说,我们提出了一个新的、可扩缩的观测模型,遵循生物模拟的地形学互动规则,导致稳定的学习和稳健的避免行为。此外,先前的工作主要侧重于援引分离原则,即设计独立于具体任务的避免碰撞原则。我们通过采用一般强化学习方法,提出了一种基于整体学习的办法,将避免碰撞与各种任务和动态结合起来。为了验证这种方法的普遍性,我们成功地将我们的方法应用于一些配置。我们所学过的政策在模拟中经过测试,随后被转移到现实世界的无人机,以验证其真实世界适用性。