Sensors and Methods for the Evaluation of Grasping

Morales, Antonio; Prats, Mario; Felip, Javier

doi:10.1007/978-1-4471-4664-3_4

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…In a recent edition of the Amazon picking challenge, the authors of [32] described problems with an open-loop control solution during pick and place tasks; therefore, autonomous, stable grasping is an essential aspect of in-hand manipulation. The authors of [33] presented a closed loop control using tactile sensing for a task of removing a book from a bookshelf when maximization of the contact surface is required.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

Fonseca

Oliveira

Petriu

2019

Sensors

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Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand object’s state requires learning object-tactile sensing data relationships. The human somatosensory system combines visual and tactile sensing information in their “What and Where” subsystem to achieve high levels of manipulation skills. The present paper proposes an approach for estimating the pose of in-hand objects combining tactile sensing data and visual frames of reference like the human “What and Where” subsystem. The system proposed here uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of underactuated fingers. While tactile sensing provides local information about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. A dual fuzzy logic controller was developed to achieve and sustain stable grasping conditions autonomously while forces were applied to in-hand objects to expose the system to different object configurations. Two sets of experiments were used to explore the system capabilities. On the first set, external forces changed the orientation of objects while the fuzzy controller kept objects in-hand for tactile and visual data collection for five machine learning estimators. Among these estimators, the ridge regressor achieved an average mean squared error of 0.077 ∘ . On the second set of experiments, one of the underactuated fingers performed open-loop object rotations and data recorded were supplied to the same set of estimators. In this scenario, the Multilayer perceptron (MLP) neural network achieved the lowest mean squared error of 0.067 ∘ .

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Section: Literature Reviewmentioning

confidence: 99%

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

Fonseca

Oliveira

Petriu

2019

Sensors

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“…This work does not discuss how the control system works with the information received from the visual system, nor the readjustment of the fingers’ position nor the equilibrium during the grasping task, Although the control system used (yellow block in Figure 3 ) was presented by Delgado et al in [ 30 ], this one implements a control system based on the kinematic model of the robot hand but without using the physical model of the manipulated object. Another approach of implementing a control system is presented in [ 8 ] (Chapter 2), which uses the physical model of the manipulated object.…”

Section: Visual Surveillance System For Robot Grasping Tasks Of Obmentioning

confidence: 99%

“…In general, the kinematics of robot systems and tactile feedback obtained directly from tactile sensors are both used to perform intelligent, dexterous manipulation but it is rare to find practical work in which visual sensors are used to check grasping tasks using robots. Nevertheless, a suitable sensing system, such as in [ 8 ], that combines tactile, force and visual sensors, allow us to adapt the grasping task in order to detect errors that can cause grasping failure . Furthermore, it evaluates the effectiveness of the manipulation process by improving the grasp of deformable objects.…”

Section: Introductionmentioning

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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“…Tactile sensing provides local information for the grasped strawberry location in a more precise way than any vision system. 19,20 This is accomplished by using PPS arrays on each finger that can sense the distribution of forces along the finger. The information from these sensors can be used to reposition the gripper in order to meet certain criteria and firmly grasp the strawberry.…”

Section: Controller Designmentioning

confidence: 99%

Design and fuzzy control of a robotic gripper for efficient strawberry harvesting

et al. 2014

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SUMMARYStrawberry is a very delicate fruit that requires special treatment during harvesting. A hierarchical control scheme is proposed based on a fuzzy controller for the force regulation of the gripper and proper grasping criteria, that can detect misplaced strawberries on the gripper or uneven distribution of forces. The design of the gripper and the controller are based on conducted experiments to measure the maximum gripping force and the required detachment force under a variety of detachment techniques. It is demonstrated that the hand motion for detaching the fruit from the stem has a significant role in the process because it can reduce the required force. By analysing those results a robotic gripper with pressure profile sensors is developed that demonstrates an efficiency comparable to the human hand for strawberry grasping. The designed gripper and fuzzy controller performance is tested with a considerable number of fresh fruits to demonstrate the effectiveness to the uncertainties of strawberry grasping.

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Sensors and Methods for the Evaluation of Grasping

Cited by 11 publications

References 20 publications

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

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

Design and fuzzy control of a robotic gripper for efficient strawberry harvesting

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