EV-Planner：基于事件的物理引导神经形态规划器实现节能型机器人导航
EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner
AI总结：
提出EV-Planner，结合事件相机与物理引导神经网络，利用浅层SNN检测目标并预测最优飞行时间，实现无人机节能避障导航。
摘要：
基于视觉的目标跟踪是执行自主空中导航以避障的重要前提。受生物学启发的神经形态事件相机正成为基于帧相机的强大替代方案，因为它们能够异步检测变化的强度（即使在光照不良的条件下），并具有高动态范围和对运动模糊的鲁棒性。脉冲神经网络（SNN）因能以节能的方式异步处理事件而受到关注。另一方面，基于物理的人工智能（AI）近期日益受到重视，因为它能够通过在传统模拟神经网络（ANN）内部进行物理建模来嵌入系统知识。在本信中，我们提出了一种基于事件的物理引导神经形态规划器（EV-Planner），利用神经形态事件相机和基于物理的AI来执行避障。我们考虑自主无人机导航任务，其任务是检测移动的闸门并从中穿过，同时避免碰撞。我们使用事件相机，以无监督的方式通过浅层脉冲神经网络执行目标检测。利用无人机旋翼中无刷直流电机的物理方程，我们训练了一个带有深度输入的轻量级能量感知物理引导神经网络（PgNN）。该网络预测最优飞行时间，负责生成接近最低能耗的路径。我们在Gazebo模拟器中生成无人机，并使用机器人操作系统（ROS）实现了一个传感器融合的从视觉到规划的神经符号框架。我们展示了安全无碰撞飞行轨迹的仿真结果，并进行了性能分析、消融研究和未来潜在研究方向探讨。
英文摘要：
Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range, and robustness to motion blur. Spiking neural networks (SNNs) have gained traction for processing events asynchronously in an energy-efficient manner. On the other hand, physics-based artificial intelligence (AI) has gained prominence recently, as they enable embedding system knowledge via physical modeling inside traditional analog neural networks (ANNs). In this letter, we present an event-based physics-guided neuromorphic planner (EV-Planner) to perform obstacle avoidance using neuromorphic event cameras and physics-based AI. We consider the task of autonomous drone navigation where the mission is to detect moving gates and fly through them while avoiding a collision. We use event cameras to perform object detection using a shallow spiking neural network in an unsupervised fashion. Utilizing the physical equations of the brushless DC motors present in the drone rotors, we train a lightweight energy-aware physics-guided neural network (PgNN) with depth inputs. This predicts the optimal flight time responsible for generating near-minimum energy paths. We spawn the drone in the Gazebo simulator and implement a sensor-fused vision-to-planning neuro-symbolic framework using Robot Operating System (ROS). Simulation results for safe collision-free flight trajectories are presented with performance analysis, ablation study and potential future research directions
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