Visual monitoring of surface deformations on objects manipulated with a robotic hand

Khalil, Fouad F.; Curtis, Phillip; Payeur, Pierre

doi:10.1109/rose.2010.5675327

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…A five-fingered robotic hand can be controlled through various methods but the master-slave controller scheme is considered to be the most widely used system. In a master-slave system, the user can control the robotic hand by using microcontrollers with preset motion settings [2] [3], visual monitoring [4] [5], haptic feedback [5] [7] and CyberGlove [8]- [10]. However, the robotics hand can also be controlled by a novel approach known as pressure sensors comparator technique.…”

Section: Overviewmentioning

confidence: 99%

Initial Development of a Master-Slave Controller for a Five-Fingered Robotic Hand Design by Using Pressure Sensors Comparator Technique

Bahrin¹,

Sahari²

2018

IJET

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There are numerous robotic hand designs but the five-fingered robotic hand design is the most dexterous robotic hand design due to its similar appearance and motions with the human hands. The five-fingered robotic hands are commonly controlled or governed through a master-slave system that can be accomplished by using simple preset motions or other complicated and advanced technologies. However, a five-fingered robotics hand can also be controlled by a novel approach known as pressure sensors comparator technique. This technique compares the values from the pressure sensors that are strategically located at the glove (master) and robotic hand (slave). If the values differ, the actuators will generate motions accordingly. The initial finding based on the master and slave prototypes showed that applying this technique is very challenging due to the humans' physiological diversity. Nevertheless, a solution was proposed for further studies and future developments by introducing an offset.

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Section: Overviewmentioning

confidence: 99%

Initial Development of a Master-Slave Controller for a Five-Fingered Robotic Hand Design by Using Pressure Sensors Comparator Technique

Bahrin¹,

Sahari²

2018

IJET

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“…Furthermore, other works are focused on detecting and tracking the deformation from the fusion of 2D images with force data [ 13 , 14 ]. Later, Khalil et al [ 15 ] used stereoscopic vision to build a 3D surface mesh from contours and colour in order to discover the deformation of non-rigid objects, and Leeper et al [ 16 ] used a low-cost stereo sensor mounted on the gripper to estimate grasp poses and to choose the best one according to a cost function based on points cloud features. More recently, Boonvisut et al [ 17 ] proposed an algorithm for the identification of the boundaries of deformable tissues, and to use it for both offline and online planning in robot-manipulation tasks.…”

Section: Related Workmentioning

confidence: 99%

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Mateo

Gil

Torres

2016

Sensors

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Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

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“…The current implementation allows for a frame rate of 6 to 10 frames/sec and performs robustly on objects without explicit visual markers. The density of points reconstructed varies with the level of deformation of the object and the inherent texture of its surface [18].…”

Section: Deformable Object 3d Shape Estimationmentioning

confidence: 99%

Integrated Multisensory Robotic Hand System for Deformable Object Manipulation

Khalil¹,

Payeur²,

Creţu

2010

IASTED Technology Conferences / 705: ARP / 706: RA / 707: NANA / 728: CompBIO

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Designing a dexterous robotic hand able to interact intelligently with deformable objects constitutes a challenging area of research where many issues are yet to be solved. The complexity of such interactions requires the assistance of intelligent multisensory robotic systems that combine measurements collected from different sensors in order to accurately plan for the forces to be applied on the deformable object. This paper presents the development of a real-time multisensory robotic hand platform that incorporates live measurements of its internal position, velocity and force parameters along with data from external tactile sensors and a stereoscopic vision device. The resulting prototype of the integrated multisensory system is validated experimentally by the computation of deformable object models in which the measurements are merged. A formal dynamic model is discussed and a neural network representation model is presented. The results demonstrate the performance and suitability of the multisensory platform for the development of enhanced robotic hand capabilities when manipulating deformable objects.

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Visual monitoring of surface deformations on objects manipulated with a robotic hand

Cited by 16 publications

References 25 publications

Initial Development of a Master-Slave Controller for a Five-Fingered Robotic Hand Design by Using Pressure Sensors Comparator Technique

Initial Development of a Master-Slave Controller for a Five-Fingered Robotic Hand Design by Using Pressure Sensors Comparator Technique

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Integrated Multisensory Robotic Hand System for Deformable Object Manipulation

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