Tactile-based manipulation of deformable objects with dynamic center of mass

Kaboli, Mohsen; Yao, Kunpeng; Cheng, Gordon

doi:10.1109/humanoids.2016.7803358

Cited by 68 publications

(48 citation statements)

References 16 publications

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“…Notwithstanding their very high sensitivity and their high conductive behavior if pressed, QTC materials still constitute a quite uncommon solution for sensorization of artificial manipulators. Further, the detection of slip by means of three-axial force information and knowledge of the static friction coefficient was lately proposed with optical sensors [41].…”

Section: A Friction-based Methodsmentioning

confidence: 99%

Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector.

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Section: A Friction-based Methodsmentioning

confidence: 99%

Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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“…The slip detection method needs the prior knowledge of an object such as weight and involves slip trials to estimate the friction coefficient of the contact surface. To reduce deformation, an upper bound of the normal force is also required [4], which is obtained through many trials or set empirically and cannot be set to the same value for all objects. The requirements and constraints make it difficult and time costly to apply these detection methods to unknown objects.…”

Section: Introductionmentioning

confidence: 99%

Force-Guided High-Precision Grasping Control of Fragile and Deformable Objects Using sEMG-Based Force Prediction

Wen

Yuan

Wang

et al. 2020

IEEE Robot. Autom. Lett.

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Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects. We aim to utilize the dexterity of human hands to regulate the contact forces for robotic hands and exploit human sensory-motor synergies in a wearable and non-invasive way. We extracted force information from the electric activities of skeletal muscles during their voluntary contractions through surface electromyography (sEMG). We built a regression model based on a Neural Network to predict the gripping force from the preprocessed sEMG signals and achieved high accuracy (R 2 = 0.982). Based on the force command predicted from human muscles, we developed a force-guided control framework, where force control was realized via an admittance controller that tracked the predicted gripping force reference to grasp delicate and deformable objects. We demonstrated the effectiveness of the proposed method on a set of representative fragile and deformable objects from daily life, all of which were successfully grasped without any damage or deformation.

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“…We verify the force condition by detecting linear slip of the object, which can be realized by measuring the increasing rate of lifting force on the contact point (Kaboli et al 2016b). The lifting force f L on one contact point is the component of the applied result force that decomposed in the Z direction of the WCF.…”

Section: A Linear Slip Detection For Force Condition Verificationmentioning

confidence: 99%

Tactile-based active object discrimination and target object search in an unknown workspace

et al. 2018

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The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active touch objects learning method that enables the robot to learn efficiently about unknown objects via their physical properties (stiffness, surface texture, and center of mass); and (3) an active object recognition strategy, based on the knowledge the robot has acquired. Furthermore, we propose a tactile-based approach for estimating the center of mass of rigid objects. Following the active touch for workspace exploration, the robotic system with the sense of touch in fingertips reduces the uncertainty of the workspace up to 65 and 70% compared respectively to uniform and random strategies, for a fixed number of samples. By means of the active touch learning method, the robot achieved 20 and 15% higher learning accuracy for the same number of training samples compared to uniform strategy and random strategy, respectively. Taking advantage of the prior knowledge obtained during the active touch learning, the robot took up to 15% fewer decision steps compared to the random method to achieve the same discrimination accuracy in active object discrimination task.

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Tactile-based manipulation of deformable objects with dynamic center of mass

Cited by 68 publications

References 16 publications

Methods and Sensors for Slip Detection in Robotics: A Survey

Methods and Sensors for Slip Detection in Robotics: A Survey

Force-Guided High-Precision Grasping Control of Fragile and Deformable Objects Using sEMG-Based Force Prediction

Tactile-based active object discrimination and target object search in an unknown workspace

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