We introduce a spherical fingertip sensor for dynamic manipulation. It is based on barometric pressure and time-of-flight proximity sensors and is low-latency, compact, and physically robust. The sensor uses a trained neural network to estimate the contact location and three-axis contact forces based on data from the pressure sensors, which are embedded within the sensor's sphere of polyurethane rubber. The time-of-flight sensors face in three different outward directions, and an integrated microcontroller samples each of the individual sensors at up to 200 Hz. To quantify the effect of system latency on dynamic manipulation performance, we develop and analyze a metric called the collision impulse ratio and characterize the end-to-end latency of our new sensor. We also present experimental demonstrations with the sensor, including measuring contact transitions, performing coarse mapping, maintaining a contact force with a moving object, and reacting to avoid collisions.

翻译：本文介绍了一种用于动态操控的球形指尖传感器。该传感器基于气压测距和飞行时间接近传感器技术，具有低延迟、紧凑和物理鲁棒性等特点。通过嵌入聚氨酯橡胶传感器球体内部的压力传感器数据，训练神经网络用于估计接触位置和三轴接触力。飞行时间传感器朝三个不同方向布置，集成微控制器以最高200 Hz的采样率对每个独立传感器进行采样。为量化系统延迟对动态操控性能的影响，我们提出并分析了称为碰撞冲量比的度量指标，并表征了新型传感器的端到端延迟。此外，还展示了该传感器的实验演示，包括接触状态转换测量、粗略映射、与运动物体保持接触力以及避免碰撞的响应操作。