Grasp Algorithms For Optotactile Robotic Sample Acquisition

Robotic sample acquisition is essentially grasping. Multi-finger robot sample grasping devices are controlled to securely pick up samples. Equations have been developed to provide optimal grasps for perfectly modeled objects, but grasping unmodeled objects like a random sample on planetary surfaces is an open research problem. Approaches to grasping unmodeled objects use various sensors, such as cameras, distributed pressure sensors, and strain gages, to characterize the object and the quality of a grasp. That information is then used to initiate or improve the grasp. A major source of difficulty in robotic grasping, therefore, is the sensing of object parameters and grasp quality. Humans combine the high information content of vision, several types of haptic/tactile sensors in the fingers (300 sensors per square centimeter), and a sophisticated learning process to grasp unknown objects. In comparison, current robotic graspers rely on a much more limited set of sensors, particularly for measuring tactile properties. This proposal focuses on an algorithm for improving grasp quality using several types of tactile information as well as the robotic grasper that can provide such information so that remote sample acquisition devices can perform as well as human sample gatherers

The human-machine interface can be used to develop novel computer input devices for art or entertainment purposes. This unique interface has additional applications in medical training dummies or orthopedic foot measurement devices. The algorithms developed can improve robustness in industrial settings. They can also be used in unstructured environments with fragile materials, such as remote explosive disposal, or nuclear materials handling. Finally, they would be useful for manipulators in complex environments, like assistive robotics in the home.

The force sensitive contact control technology has many potential applications within NASA. This technology includes innovative pattern sensing capabilities that provide a novel, highly accurate and sensitive human-machine interface. It also has the potential for greater accuracy in force measurement and control in a different regime than current sensor (strain gauge) technologies and can be a new tool in the machine designer's toolkit. This technology will provide NASA with advanced, robust grasping solutions for unstructured environments. This will include both the computer vision algorithms that extract grasp quality metrics from an optotactile fingerpad, as well as the grasp algorithms that take advantage of that information. We will also investigate commercializing a monolithic hardware solution for an optotactile grasper, with a single board solution for image capture, lighting, and data processing.

This is a mature technology (TRL 8) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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