Hand closure model for planning top grasps with soft robotic hands

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

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EFFICIENT GRASP PLANNING WITH REACHABILITY ANALYSIS

International Journal of Humanoid Robotics ◽

10.1142/s0219843611002654 ◽

2011 ◽

Vol 08 (04) ◽

pp. 761-775 ◽

Cited By ~ 8

Author(s):

ZHIXING XUE ◽

RUEDIGER DILLMANN

Keyword(s):

Efficient Algorithm ◽

Reachability Analysis ◽

Robotic Arm ◽

Robotic Hands ◽

Grasp Planning ◽

Grasping Forces ◽

Force Closure ◽

Geometrical Information ◽

Kinematic Information

Grasping can be seen as two steps: placing the hand at a grasping pose and closing the fingers. In this paper, we introduce an efficient algorithm for grasping pose generation. By the use of preshaping and eigen-grasping actions, the dimension of the space of possible hand configurations is reduced. The object to be grasped is decomposed into boxes of a discrete set of different sizes. By performing finger reachability analysis on the boxes, the kinematic feasibility of a grasp can be determined. If a reachable grasp is force-closure and can be performed by the robotic arm, its grasping forces are optimized and can be executed. The novelty of our algorithm is that it takes into account both the object geometrical information and the kinematic information of the hand to determine the grasping pose, so that a reachable grasping pose can be found very quickly. Real experiments with two different robotic hands show the efficiency and feasibility of our method.

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Backlash Robotic Systems Compensation by Inverse Model-Based PID Control

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2019.8926953 ◽

2019 ◽

Author(s):

Fernando da Fonseca Schneider ◽

Cesar Bastos da Silva ◽

Paulo Jefferson Dias de Oliveira Evald ◽

Rodrigo Sousa e Silva ◽

Rodrigo Zelir Azzolin

Keyword(s):

Pid Control ◽

Inverse Model ◽

Robotic Systems ◽

Model Based

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Automated Design of Robotic Hands for In-Hand Manipulation Tasks

International Journal of Humanoid Robotics ◽

10.1142/s0219843619500294 ◽

2020 ◽

Vol 17 (01) ◽

pp. 1950029

Author(s):

Christopher Hazard ◽

Nancy Pollard ◽

Stelian Coros

Keyword(s):

Kinematic Model ◽

Automated Design ◽

Robotic Hand ◽

Motion Synthesis ◽

Robotic Hands ◽

Dexterous Manipulation ◽

Grasp Planning ◽

User Input ◽

High Level

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

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