Table tennis is a fast-paced and exhilarating sport that demands agility, precision, and fast reflexes. In recent years, robotic table tennis has become a popular research challenge for robot perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. Previous approaches have employed conventional frame-based cameras with Convolutional Neural Networks (CNNs) or traditional computer vision methods. In this paper, we propose a novel solution that combines an event-based camera with Spiking Neural Networks (SNNs) for ball detection. We use multiple state-of-the-art SNN frameworks and develop a SNN architecture for each of them, complying with their corresponding constraints. Additionally, we implement the SNN solution across multiple neuromorphic edge devices, conducting comparisons of their accuracies and run-times. This furnishes robotics researchers with a benchmark illustrating the capabilities achievable with each SNN framework and a corresponding neuromorphic edge device. Next to this comparison of SNN solutions for robots, we also show that an SNN on a neuromorphic edge device is able to run in real-time in a closed loop robotic system, a table tennis robot in our use case.

使用事件驱动摄像头与脉冲神经网络相结合的新颖解决方案，实现了乒乓球的精确检测。通过比较不同脉冲神经网络框架和相应神经形态边缘设备的精确性和运行时间，为机器人研究人员提供了一个基准，并证明神经形态边缘设备上的脉冲神经网络能够实时运行在乒乓球机器人系统中。

使用基于事件相机的神经形态硬件设备的脉冲神经网络进行快速运动目标检测