Surface grasping with bio-inspired, opposed adhesion
Abstract/Contents
- Abstract
- Grasping is central to robotics, and numerous hands and grippers have been developed for applications ranging from personal service robots to industrial manufacturing lines. Either by wrapping around an object to support it from underneath or by using internal forces and friction, these traditional grippers can grasp many objects securely. However, this approach does not work when the object is much larger than the hand. In this case, adhesion becomes a solution. Various adhesive or astrictive technologies have been applied in robotics, including suction, magnetic forces and electrostatic forces. Most of these technologies require active control and consume power. Many are also limited to a range of surface properties or materials. In nature, we find alternative solutions including arrays of small spines for attaching to rough surfaces and structures with microscopic features that can attach to smooth surfaces using van der Waals forces. For example, many insects use tiny spines and claws on their legs to catch on small bumps and pits (asperities) on rough surfaces for climbing and grasping. Gecko lizards have demonstrated van der Waals adhesion on smooth surfaces using fine hairs, or setae, on the feet. These types of adhesion do not require power and have been proven strong, reusable, controllable, and adaptive to a wide range of surfaces. Learning from biology and using bio-inspired adhesion can facilitate robust grasping of large objects. Previous work has considered bio-inspired adhesion for climbing robots, where shear loads dominate. This thesis focuses on adhesion for grasping and manipulation so that the robot can apply forces and moments in any direction. The work expands the modeling and analysis of bio-inspired, directional adhesion, introduces an opposed-grip strategy for grasping and manipulation, and presents efficient scaling methods to accommodate large forces and moments with special considerations for dynamic impacts. For insect-inspired microspines, a statistical model describes the asperity spatial and strength distributions and predicts the shear and normal adhesion capabilities of a group of microspines. Various opposed-grip mechanisms are designed and modeled for grasping a range of rough surfaces such as concrete, asphalt and the bark of trees. The stochastic models of spine/surface interaction lead to insights for designing grippers that work on a range of surfaces. A new opposed-spine gripper is demonstrated on a small aerial platform, allowing it to perch on building walls and ceilings to save power and greatly extend mission life. For gecko-inspired adhesives, similar opposed mechanisms are developed for both flat and curved surfaces. Based on the insights obtained from modeling arrays of directional adhesive tiles or films, a series of grippers are shown to have significantly increased adhesion capabilities as compared to simply increasing the area for a single adhesive patch. For large loads, different scaling strategies, such as a pulley differential and constant force springs, are developed and compared for various applications. Outriggers are added to grippers to enlarge moment capabilities for manipulation. A passive, nonlinear wrist, in conjunction with scaled grippers, is presented to facilitate efficient energy absorption during dynamic impacts while also remaining stiff for precise manipulation and providing overload protection for regular operations. An integrated gripper with the above elements demonstrates grasping and manipulation of objects several times larger than the gripper itself. The limiting force and moment space of such a gripper can be computed, providing useful information for robotic motion planning and control. A series of experiments on floating platforms, conducted with the NASA Jet Propulsion Laboratory, demonstrates the applicability of this approach for grasping and manipulating objects in space.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Jiang, Hao |
|---|---|
| Associated with | Stanford University, Department of Mechanical Engineering. |
| Primary advisor | Cutkosky, Mark R |
| Thesis advisor | Cutkosky, Mark R |
| Thesis advisor | Follmer, Sean |
| Thesis advisor | Pavone, Marco, 1980- |
| Advisor | Follmer, Sean |
| Advisor | Pavone, Marco, 1980- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Hao Jiang. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Hao Jiang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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