Dynamic regrasping by in-hand orienting of grasped objects using non-dexterous robotic grippers

Sintov, Avishai; Shapiro, Amir

doi:10.1016/j.rcim.2017.09.009

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…Such assumption can be overcome by maintaining controlled friction through contact with the object to prevent it from tilting. Alternatively, integrating a control method 13 can compensate tilting of the object due to misalignment. A control method can also act to balance the object at the same orientation.…”

Section: Discussionmentioning

confidence: 99%

“…The end effector allows relative velocity (by throwing or sliding) with the object through a series of dynamic manipulations. [11][12][13][14] Some work done in this field use a multi-fingered highly dexterous hand for performing regrasping. The work of Furukawa et al 15 proposed a regrasping strategy based on the visual feedback of the manipulated object, with a multi-fingered hand.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal Regrasping of Elongated Objects

2019

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SUMMARYRegrasping is a manipulation to alternate between grasp configurations of an object to perform different tasks. We address a regrasping problem termed longitude regrasping to reposition a gripper along an elongated object. We propose an algorithm using the dynamics of the arm and a non-dexterous gripper to perform the manipulation. Energy control is used to toss the object up and catch it under the goal position. Clipped Linear Quadratic Regulator control approach is then applied to the gripper jaws to control the friction force on the object and to let it slide to the final goal position. The object sliding within the gripper is modeled as a semi-active linear joint where only dissipative forces can be applied to it. A set of experiments validated the feasibility of the method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Longitudinal Regrasping of Elongated Objects

2019

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“…Dynamic regrasping is a method of changing the pose of an object by releasing the grasped object, then grasping it again with a different pose [26], [27]. In [28], a robot throws an object upward using a multi-fingered hand that can operate at a high speed, detects the object with a high-speed camera, and regrasps the object.…”

Section: Related Workmentioning

confidence: 99%

Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Arai¹,

Konada²,

Yoshinaga³

et al. 2021

Preprint

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<div>This study proposes a method of robust regrasping an object using a dual-arm robot with general-purpose hands, which is robust against the error of grasping. In this paper, one arm is assigned to hand over the object to the other arm that is named a receiver arm. The grasping error must be considered to increase the success rate of the regrasping since a hand-over arm first picks up the object with the general-purpose hand. In an online phase, the proposed method performs object positioning at an optimal pose at the time of regrasping using an image-based visual servoing (IBVS) approach to reduce the effect of the grasping error. In the planning phase, the proposed method computes the optimal pose for regrasping by maximizing the minimum singular values of the image Jacobian of IBVS to achieve a high positioning accuracy using a 3D model of the target object. To achieve the regrasping objects with various shapes robustly against image noises and changes in light environments, the image Jacobian of IBVS is computed by numerical differential using an actual data set. A large number of data sets corresponding to each candidate grasp are usually required for computing the image Jacobian. To reduce the number of data sets, we propose a conversion method of the image Jacobian requiring only one data set corresponding to one representative grasp. The experimental results show that the proposed method achieves regrasping of target objects with the general-purpose hands with high success rates and performs target object positioning with less than 0.7[mm] positioning error.</div>

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“…There are many studies conducted on robot control and trajectory generation. A number of studies have been conducted in object grasping [ [44] , [45] , [46] ], which is closely related to our application. For instance, in Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Segmenting areas of potential contamination for adaptive robotic disinfection in built environments

Zhong

Ли

et al. 2020

Building and Environment

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Mass-gathering built environments such as hospitals, schools, and airports can become hot spots for pathogen transmission and exposure. Disinfection is critical for reducing infection risks and preventing outbreaks of infectious diseases. However, cleaning and disinfection are labor-intensive, time-consuming, and health-undermining, particularly during the pandemic of the coronavirus disease in 2019. To address the challenge, a novel framework is proposed in this study to enable robotic disinfection in built environments to reduce pathogen transmission and exposure. First, a simultaneous localization and mapping technique is exploited for robot navigation in built environments. Second, a deep-learning method is developed to segment and map areas of potential contamination in three dimensions based on the object affordance concept. Third, with short-wavelength ultraviolet light, the trajectories of robotic disinfection are generated to adapt to the geometries of areas of potential contamination to ensure complete and safe disinfection. Both simulations and physical experiments were conducted to validate the proposed methods, which demonstrated the feasibility of intelligent robotic disinfection and highlighted the applicability in mass-gathering built environments.

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Dynamic regrasping by in-hand orienting of grasped objects using non-dexterous robotic grippers

Cited by 17 publications

References 14 publications

Longitudinal Regrasping of Elongated Objects

Longitudinal Regrasping of Elongated Objects

Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Segmenting areas of potential contamination for adaptive robotic disinfection in built environments

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