Finger Readjustment Algorithm for Object Manipulation Based on Tactile Information

Ramón, Juan Antonio Corrales; Torres, Fernando; Perdereau, Véronique

doi:10.5772/53561

Cited by 8 publications

(4 citation statements)

References 16 publications

(24 reference statements)

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“…Similar to tactile exploration, tactile grasping is also an important method to extract object properties. Some researchers extracted the contact level information [81], [91] and object level information [89], [105], [110], [126] Tactile Exploration analytical models [66], [97] [123], [156], [157] [72], [138], [158] data-driven [75] [159] [160] [161] Grasping analytical models [81], [91] [89], [105], [110], [126] [162], [163] data-driven [164], [165] [166], [167] [134], [168]- [170] In-hand Manipulation analytical models [171] [172] [173], [174], [137], [175], [176], [177] data-driven [154] [178] [179], [151] Tool Manipulation analytical models [180] [181] data-driven [29] [139] [182] [183], [143], [153] Locomotion analytical models [184], [95], [185], [186] -…”

Section: B Graspingmentioning

confidence: 99%

“…For this task, some work focused on the computation of contact and object level information [171], [172], [154], [178], but the majority of work was to study the tactile controllers to relocate the grasped objects. The controllers have been performed with either multifingered robot hand [23], [173], [177], [204]- [207] or grippers [208]- [210].…”

Section: In-hand Manipulationmentioning

confidence: 99%

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A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

187

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Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data is however not limited to individual contacts and can be used to extract a wide range of information about the objects in the robots environment as well as the robots own actions during the interactions.In this paper, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The paper also includes an overview of different types of robot applications and the types of tactile information that they employ.The paper concludes with a discussion of tactile-based computational framework and future tactile applications which are still beyond current robot's capabilities.

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Section: B Graspingmentioning

confidence: 99%

Section: In-hand Manipulationmentioning

confidence: 99%

A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

187

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“…Nowadays, manipulation has become an increasingly important standing research topic in robotics. Most of the related works in this field consider the grasping of rigid bodies as an extensively studied area, which is rich with theoretical analysis and implementations using different robotic hands ( [1][2][3][4][5][6][7][8]). Robotic grasping of deformable objects has also acquired importance recently due to several potential applications in various areas, including biomedical processing, the food processing industry, service robotics, robotized surgery, etc.…”

Section: Introductionmentioning

confidence: 99%

Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems

Zaidi¹,

Ramón

Sabourin

et al. 2020

Applied Sciences

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In the grasping and manipulation of 3D deformable objects by robotic hands, the physical contact constraints between the fingers and the object have to be considered in order to validate the robustness of the task. Nevertheless, previous works rarely establish contact interaction models based on these constraints that enable the precise control of forces and deformations during the grasping process. This paper considers all steps of the grasping process of deformable objects in order to implement a complete grasp planning pipeline by computing the initial contact points (pregrasp strategy), and later, the contact forces and local deformations of the contact regions while the fingers close over the grasped object (grasp strategy). The deformable object behavior is modeled using a nonlinear isotropic mass-spring system, which is able to produce potential deformation. By combining both models (the contact interaction and the object deformation) in a simulation process, a new grasp planning method is proposed in order to guarantee the stability of the 3D grasped deformable object. Experimental grasping experiments of several 3D deformable objects with a Barrett hand (3-fingered) and a 6-DOF industrial robotic arm are executed. Not only will the final stable grasp configuration of the hand + object system be obtained, but an arm + hand approaching strategy (pregrasp) will also be computed.

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“…Tactile sensors provide important contactlevel information such as contact geometry, force, material properties, and contact events (Cutkosky, Howe and Provancher, 2008). Tactile feedback was used, e.g., in tactile exploration of object properties (Lepora, Martinez-Hernandez and Prescott, 2016), grasping (Bohg et al, 2014), object and tool manipulation (Chebotar, Kroemer and Peters, 2014;Ramón, Medina and Perdereau, 2013).…”

Section: Introductionmentioning

confidence: 99%