Bi-Modal Hemispherical Sensors for Dynamic Locomotion and Manipulation

Abstract

The ability to measure multi-axis contact forces and contact surface normals in real time is critical to allow robots to improve their dexterous manipulation and locomotion abilities. This paper presents a new fingertip sensor for 3-axis contact force and contact location detection, as well as improvements on an existing footpad sensor through use of a new artificial neural network estimator. The fingertip sensor is intended for use in manipulation, while the footpad sensor is intended for high force use in locomotion. Both sensors consist of pressure sensing elements embedded within a rubber hemisphere, and utilize an artificial neural network to estimate the applied forces (f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> , and f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> ), and contact angles (θ and φ) from the individual sensor element readings. The sensors are inherently robust, and the hemispherical shape allows for easy integration into point feet and fingertips. Both the fingertip and footpad sensors demonstrate the ability to track forces and angles accurately over the surface of the hemisphere (θ=±45° and φ=±45°) and can experience up to 25N and 450N normal force, respectively, without saturating. The performance of the sensor is demonstrated with experimental results of dynamic control of a robotic arm with real-time sensor feedback.