Shape tolerance for robot gripper jaws

Zhang, T.; Cheung, L.; Goldberg, Ken

doi:10.1109/iros.2001.977236

Cited by 7 publications

(7 citation statements)

References 17 publications

(18 reference statements)

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“…Additionally, a lot of research has been devoted to the design of gripper mechanisms (Krenich, 2004;Cuadrado et al, 2004;Lanni and Ceccarelli, 2009;Ceccarelli et al, 2002). Some work has been done on the topic of gripper jaw design (Zhang et al, 2001; Zhang and Goldberg, 2006).…”

Section: Gripper Design In Industrymentioning

confidence: 99%

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Wolniakowski

Gams

Kiforenko

et al. 2018

Acta Mechanica Et Automatica

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The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

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Section: Gripper Design In Industrymentioning

confidence: 99%

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Wolniakowski

Gams

Kiforenko

et al. 2018

Acta Mechanica Et Automatica

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“…Aligning objects enables more secure grasps and by that enables more accurate placement. Gripper jaw design for object alignment was investigated in [14][15][16][17]. In [16] they define a modular gripper surface based on trapezoidal segments for which they present an algorithm that can optimize the gripper design such that a specific alignment of the object is obtained when it is grasped from the top.…”

Section: Quantitative Evaluation Of Gripper Designsmentioning

confidence: 99%

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Wolniakowski

Miatliuk

Gosiewski

et al. 2017

J Intell Robot Syst

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In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a parallel finger type gripper described by twelve parameters. We furthermore present a parametrization of the grasping task and context, which is essential as an input to the computation of gripper performance. We exemplify important aspects of the indices by looking at their performance on subsets of the parameter space by discussing the decoupling of parameters and show optimization results for two use cases for different task contexts. We provide a qualitative evaluation of the obtained results based on existing design guidelines and our engineering experience. In addition, we show that with our method we achieve superior alignment properties compared to a naive approach with a cutout based on the "inverse of an object". Furthermore, we provide an experimental evaluation of our proposed method by verifying the simulated grasp outcomes through a real-world experiment.

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“…The work in robotic grasping that addresses tolerance largely focuses on part pose [4] [13] [29]. Methods for sensorless part orientation [5] [17] [37] can also be used in the presence of uncertain part pose. However, these methods do not take into account tolerances for the geometry of the part.…”

Section: Related Workmentioning

confidence: 99%

Estimating part tolerance bounds based on adaptive Cloud-based grasp planning with slip

Kehoe

Berenson

Goldberg

2012

2012 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-We explore setting bounds on part tolerances based on an adaptive Cloud-based algorithm to estimate lower bounds on achieving force closure during grasping. We consider the most common robot gripper: a pair of thin parallel jaws, and a conservative class of push-grasps allowing slip that can enhance part alignment for parts that can be modeled as extruded polygons. The grasp analysis algorithm takes as input a set of candidate grasps and perturbations of a nominal part shape. We define a grasp quality metric based on a lower bound on the probability of achieving force closure. We present two extensions to our previous highly-parallelizable algorithm that adaptively reduce the number of grasp evaluations and improve the lower bound by including slip. We develop a procedure for finding the effect of increasing tolerance in vertices on grasp quality, which allows part tolerances to be bounded to ensure minimum grasp quality levels. We find that including slip improves grasp quality estimates by 16%, and our adaptive extension reduces grasp evaluations by 91.5% while maintaining 92.6% of grasp quality.

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Shape tolerance for robot gripper jaws

Abstract: In [19]

Cited by 7 publications

References 17 publications

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Estimating part tolerance bounds based on adaptive Cloud-based grasp planning with slip

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