Finding Optimal Independent Grasp Regions of Parallel Manipulators with Additional Applications for Limbed Robot Mobility

Metadata Updated: February 28, 2019

For the problem of robotic manipulation, wherein a robotic manipulator interacts with objects or its environment using an end-effector (gripper), there have been attempts to quantify and optimize how good a hold the gripper has on an object being manipulated using grasp quality metrics. Traditionally, grasp quality analysis assumes a rigid gripper and a rigid object. However, technologies currently being investigated by NASA including climbing robots, robotic transport of surface assets, and robotic grappling of free floating objects rely on compliant grippers, and grippers who primarily apply pulling and shear loads. Of particular interest are microspine and gecko adhesive based grippers currently under development. For such grippers, traditional grasp quality analyses do not apply. The first objective of this proposal is to develop a stiffness based approach to analyze grasp quality accompanied by the theoretical background to evaluate its effectiveness.

It is common to use multiple grippers in parallel to perform manipulation tasks. In the case of climbing robot locomotion it is necessary, as each limb/gripper must grasp the surface and work together to move along its trajectory. To accommodate uncertainties, it is desirable to find grasp regions, rather than single grasps, in which the grippers can be placed to achieve their tasks. A novel method for finding such regions is to frame the problem as a bipartite graph problem, where graph edges represent grasp qualities. From this basis, finding grasp regions equates to finding bicliques corresponding to regions of desirable surface geometry. The next objective of this proposal is to develop algorithms for finding regions that are practically useful for grasping, by utilizing and expanding on current graphical and manipulation algorithms.

Practical implementations of these algorithms must allow for time-efficient computation of grasps for use in robot planning. The objective is to use techniques including probabilistic methods and example-informed searches to efficiently generate grasp regions that meet or exceed a minimum quality threshold (safety factor) in real time.

Finally, it is sometimes necessary to perform in-situ re-grasps. For a climbing robot a grasp may begin to slip, or perform worse than expected, and re-grasping of the surface will be necessary. The objective is to use tools such as in-situ pull testing to detect poor grasps, and develop methods for using this information as feedback to effectively execute a re-grasp.

Development of this technology directly impacts NASA interests by supplying methods of analyzing and planning for robot manipulation and climbing robot tasks, thereby advancing crucial technologies for robotic space operations and exploration.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Dates

Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_93864
Maintainer
TECHPORT SUPPORT
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 01918c5c-9686-4aaa-82e0-686096e9b917
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2021-09-01
Homepage URL https://techport.nasa.gov/view/93864
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 9c03ba2721a73a39b4235d71e3841d9d7a4cc595
Source Schema Version 1.1

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