Abstract-We describe a combined force and distance sensor using a commodity infrared distance sensor embedded in a transparent elastomer with applications in robotic manipulation. Prior to contact, the sensor works as a distance sensor (0-10 cm), whereas after contact the material doubles as a spring, with force proportional to the compression of the elastomer (0-5 N). We describe its principle of operation and design parameters, including polymer thickness, mixing ratio, and emitter current, and show that the sensor response has an inflection point at contact that is independent of an object's surface properties. We then demonstrate how two arrays of eight sensors, each mounted on a standard Baxter gripper, can be used to (1) improve gripper alignment during grasping, (2) determine contact points with objects, and (3) obtain crude 3D models that can serve to determine possible grasp locations.
We describe the grasping and manipulation strategy that we employed at the autonomous track of the Robotic Grasping and Manipulation Competition at IROS 2016. A salient feature of our architecture is the tight coupling between visual (Asus Xtion) and tactile perception (Robotic Materials), to reduce the uncertainty in sensing and actuation. We demonstrate the importance of tactile sensing and reactive control during the final stages of grasping using a Kinova Robotic arm. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).
The lack of sensory feedback provided by prosthetic hands dramatically limits the utility of the device. Peripheral nerve interfaces are now able to produce stable somatosensory percepts for upper limb amputees. Sensors must be able to detect forces across the fingers of the prosthesis in a repeatable and reliable fashion. We solved this concern with a novel multi-modal tactile sensor which consists of an infrared proximity sensor and a barometric pressure sensor embedded in an elastomer layer with potential use in prosthetic devices. Signals from both sensors measure proximity (0-10 mm), contact (0 N), and force (0-50 N) and are combined to localize impact at five spatial locations and three angles of incidence. Here, we describe the sensor design, its characterization, and data analysis. We use Gaussian process regression to fuse the signals from both sensors to obtain calibrated force in Newton with an R 2 value of 0.99. We use supervised learning to localize probe position and direction with classification accuracies of 96% and 89%, respectively. The complementary nature of both sensors leads to several sensing modalities that no one sensor can provide on its own and the repeatable, reliable, and compact form of the sensor enables use in multi-functional prosthetic hands.
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