Independent Contact Regions based on a patch contact model

Charusta, Krzysztof; Krug, Robert; Dimitrov, Dimitar; Iliev, Boyko

doi:10.1109/icra.2012.6225325

Cited by 13 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore it has been recognized, that the set of exertable forces/moments should be augmented accordingly [19], [20]. In this work, we follow the approach in [20] and apply generalized frictional forces, constrained by (1), at points c ∈ C i lying in the contact surface area. We Fig.…”

Section: Grasp Contact Modelingmentioning

confidence: 99%

Analytic grasp success prediction with tactile feedback

Krug

Lilienthal

Kragić³

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

show abstract

Section: Grasp Contact Modelingmentioning

confidence: 99%

Analytic grasp success prediction with tactile feedback

Krug

Lilienthal

Kragić³

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For some applications, a more realistic model of the contact area might be more desirable than only the contact area centers and radii that our method currently measures. For example, in robotics, a patch contact model improves grasping compared with a point model ( Charusta et al, 2012 ). Our method is also extendable to measuring the contact areas, by detecting the contact areas in the infrared camera images and mapping them to the model surface.…”

Section: Discussionmentioning

confidence: 99%

A Method for Measuring Contact Points in Human–Object Interaction Utilizing Infrared Cameras

Hakala

Häkkinen

2022

Front. Robot. AI

View full text Add to dashboard Cite

This article presents a novel method for measuring contact points in human–object interaction. Research in multiple prehension-related fields, e.g., action planning, affordance, motor function, ergonomics, and robotic grasping, benefits from accurate and precise measurements of contact points between a subject’s hands and objects. During interaction, the subject’s hands occlude the contact points, which poses a major challenge for direct optical measurement methods. Our method solves the occlusion problem by exploiting thermal energy transfer from the subject’s hand to the object surface during interaction. After the interaction, we measure the heat emitted by the object surface with four high-resolution infrared cameras surrounding the object. A computer-vision algorithm detects the areas in the infrared images where the subject’s fingers have touched the object. A structured light 3D scanner produces a point cloud of the scene, which enables the localization of the object in relation to the infrared cameras. We then use the localization result to project the detected contact points from the infrared camera images to the surface of the 3D model of the object. Data collection with this method is fast, unobtrusive, contactless, markerless, and automated. The method enables accurate measurement of contact points in non-trivially complex objects. Furthermore, the method is extendable to measuring surface contact areas, or patches, instead of contact points. In this article, we present the method and sample grasp measurement results with publicly available objects.

show abstract

“…The higher the volume of this grasp wrench space, the better the grasp. In order to visualize the effect of increased size of the contact patches provided by the hyper adaptive fingers defined in this paper, we calculate the GWS of patch contacts instead of the point contact model (Charusta et al, 2012). If the hand makes a patch contact centered at point p i , the patch is defined as eqnarrayright center leftPfalse(i,rfalse)=false{z : δiz≤r,z∈Ofalse}…”

Section: Grasp Quality Measuresmentioning

confidence: 99%

On Alternative Uses of Structural Compliance for the Development of Adaptive Robot Grippers and Hands

et al. 2019

View full text Add to dashboard Cite

Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexures joints) or in their finger-pads. In this paper, we present a series of alternative uses of structural compliance for the development of simple, adaptive, compliant and/or under-actuated robot grippers and hands that can efficiently and robustly execute a variety of grasping and dexterous, in-hand manipulation tasks. The proposed designs utilize only one actuator per finger to control multiple degrees of freedom and they retain the superior grasping capabilities of the adaptive grasping mechanisms even under significant object pose or other environmental uncertainties. More specifically, in this work, we introduce, discuss, and evaluate: (a) a design of pre-shaped, compliant robot fingers that adapts/conforms to the object geometry, (b) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability, and (c) a design that executes compliance adjustable manipulation tasks that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads. The grippers are experimentally tested and their efficiency is validated using three different types of tests: (i) grasping tests that involve different everyday objects, (ii) grasp quality tests that estimate the contact area between the grippers and the objects grasped, and (iii) dexterous, in-hand manipulation experiments to evaluate the manipulation capabilities of the Compliance Adjustable Manipulation (CAM) hand. The devices employ mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks.

show abstract

Independent Contact Regions based on a patch contact model

Cited by 13 publications

References 22 publications

Analytic grasp success prediction with tactile feedback

Analytic grasp success prediction with tactile feedback

A Method for Measuring Contact Points in Human–Object Interaction Utilizing Infrared Cameras

On Alternative Uses of Structural Compliance for the Development of Adaptive Robot Grippers and Hands

Contact Info

Product

Resources

About