Autonomous robotic systems have gained a lot of attention, in recent years. However, accurate prediction of robot motion in indoor environments with limited visibility is challenging. While vision-based and light detection and ranging (LiDAR) sensors are commonly used for motion detection and localization of robotic arms, they are privacy-invasive and depend on a clear line-of-sight (LOS) for precise measurements. In cases where additional sensors are not available or LOS is not possible, these technologies may not be the best option. This paper proposes a novel method that employs channel state information (CSI) from WiFi signals affected by robotic arm motion. We developed a convolutional neural network (CNN) model to classify four different activities of a Franka Emika robotic arm. The implemented method seeks to accurately predict robot motion even in scenarios in which the robot is obscured by obstacles, without relying on any attached or internal sensors.

翻译：自主机器人系统近年来受到了广泛关注。然而，在能见度有限的室内环境中准确预测机器人运动仍具有挑战性。虽然基于视觉和光探测与测距（LiDAR）的传感器常用于机械臂的运动检测与定位，但它们会侵犯隐私，且依赖清晰的视线以获取精确测量。在缺乏额外传感器或无法实现视线传输的情况下，这些技术可能并非最佳选择。本文提出一种新方法，利用受机械臂运动影响的WiFi信号中的信道状态信息（CSI）。我们开发了一个卷积神经网络（CNN）模型，用于对Franka Emika机械臂的四种不同活动进行分类。该方法旨在即使机器人被障碍物遮挡且不依赖任何外接或内部传感器的情况下，也能准确预测机器人运动。