用事件相机+强化学习让无人机自动追目标，反应快还抗干扰。
Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking
- 直接从事件流输入到飞行控制，端到端训练无人机追踪。
- 在快速移动和变光环境下仍能稳定追踪，泛化能力强。
- 适合做低功耗、高动态场景下的自主无人机系统。
本文提出一种基于事件相机的无人机主动追踪方法，旨在提升无人飞行器（UAV）的自主性。事件相机作为低功耗成像传感器，具备高速响应与宽动态范围优势。所设计的追踪控制器通过实时接收事件传感器反馈，动态调整无人机运动以持续跟踪目标。为充分利用四旋翼无人机的运动能力及事件传感器特性，我们构建了一个端到端深度强化学习（DRL）框架，直接将原始事件流映射为飞行控制指令。为在复杂多变环境中学习最优策略，采用带有领域随机化的仿真环境，实现模型向真实世界的有效迁移。实验表明，该方法在快速移动目标和光照变化等挑战性场景中表现优异，显著提升了模型的泛化能力。
原文摘要 · Abstract (English)
In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that offer significant advantages in speed and dynamic range. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target. To leverage the full motion capabilities of a quadrotor and the unique properties of event sensors, we propose an end-to-end deep-reinforcement learning (DRL) framework that maps raw sensor data from event streams directly to control actions for the UAV. To learn an optimal policy under highly variable and challenging conditions, we opt for a simulation environment with domain randomization for effective transfer to real-world environments. We demonstrate the effectiveness of our approach through experiments in challenging scenarios, including fast-moving targets and changing lighting conditions, which result in improved generalization capabilities.
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