Robust Detection of Absence of Slip in Robot Hands and Feet

Massalim, Yerkebulan; Kappassov, Zhanat; Varol, Hüseyin Atakan; Hayward, Vincent

doi:10.1109/jsen.2021.3127501

Cited by 9 publications

(6 citation statements)

References 59 publications

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“…By virtue of its compactness, the code simplifies and accelerates the decoding by the nervous system, which is needed to react promptly while avoiding slippage of an object in hand. Even if the existence of a compact lexicon in the human nervous system still needs to be confirmed, the Eigenstrain decomposition can be directly used to design efficient control policies for robotic grippers that can manipulate objects while preventing slippage instead of reacting to it 38 – 40 .…”

Section: Discussionmentioning

confidence: 99%

Efficient tactile encoding of object slippage

Willemet

Huloux

Wiertlewski

2022

Sci Rep

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When grasping objects, we rely on our sense of touch to adjust our grip and react against external perturbations. Less than 200 ms after an unexpected event, the sensorimotor system is able to process tactile information to deduce the frictional strength of the contact and to react accordingly. Given that roughly 1,300 afferents innervate the fingertips, it is unclear how the nervous system can process such a large influx of data in a sufficiently short time span. In this study, we measured the deformation of the skin during the initial stages of incipient sliding for a wide range of frictional conditions. We show that the dominant patterns of deformation are sufficient to estimate the distance between the frictional force and the frictional strength of the contact. From these stereotypical patterns, a classifier can predict if an object is about to slide during the initial stages of incipient slip. The prediction is robust to the actual value of the interfacial friction, showing sensory invariance. These results suggest the existence of a possible compact set of bases that we call Eigenstrains. These Eigenstrains are a potential mechanism to rapidly decode the margin from full slip from the tactile information contained in the deformation of the skin. Our findings suggest that only 6 of these Eigenstrains are necessary to classify whether the object is firmly stuck to the fingers or is close to slipping away. These findings give clues about the tactile regulation of grasp and the insights are directly applicable to the design of robotic grippers and prosthetics that rapidly react to external perturbations.

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Section: Discussionmentioning

confidence: 99%

Efficient tactile encoding of object slippage

Willemet

Huloux

Wiertlewski

2022

Sci Rep

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“…Even today this consideration is important for users. While some publications report algorithms have a low power consumption, they do not supply an estimate for the required processing power, for example [72]. Its feasibility is thus difficult to gauge.…”

Section: Detection Methodsmentioning

confidence: 99%

Slip Detection Strategies for Automatic Grasping in Prosthetic Hands

Kyberd

2023

Sensors

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The detection of an object slipping within the grasp of a prosthetic hand enables the hand to react to ensure the grasp is stable. The computer controller of a prosthetic hand needs to be able to unambiguously detect the slide from other signals. Slip can be detected from the surface vibrations made as the contact between object and terminal device shifts. A second method measures the changes in the normal and tangential forces between the object and the digits. After a review of the principles of how the signals are generated and the detection technologies are employed, this paper details the acoustic and force sensors used in versions of the Southampton Hand. Attention is given to the techniques used in the field. The performance of the Southampton tube sensor is explored. Different surfaces are slid past a sensor and the signals analysed. The resulting signals have low-frequency content. The signals are low pass filtered and the resulting processing results in a consistent response across a range of surfaces. These techniques are fast and not computationally intensive, which makes them practical for a device that is to be used daily in the field.

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“…The problem statement of this article deals with the implementation and investigation of advanced ML techniques based on the fuzzy sets theory, theory of neuro system, statistical learning theory, and others, for the increasing efficiency of robot sensor and control information processing in multi-component robotic complexes that function in non-stationary, uncertain, or unknown working environments. The missions of the considered MCRC may deal with the preliminary unknown or changeable mass of the manipulated objects and with the need to avoid obstacles or to correct the adaptive robot’s path in collisions with obstacles [ 3 , 6 , 7 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ]. In this case, the adaptive robot with a fixed base should have the possibility to identify object mass and directions of the manipulated object slippage.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

“…One of the efficient approaches to determine the unknown mass of a manipulated object and the desired value of clamping force is the use of tactile sensors that provide detection of the object slippage between the gripper fingers [ 3 , 6 , 12 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 100 , 101 , 102 ]. Besides this, slip displacement information can be used to recognize objects from a set of alternatives and for correction of the control algorithm and robot gripper’s trajectory.…”

Section: The Machine Learning Algorithms For Extension Of Functional ...mentioning

confidence: 99%

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

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Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

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Robust Detection of Absence of Slip in Robot Hands and Feet

Cited by 9 publications

References 59 publications

Efficient tactile encoding of object slippage

Efficient tactile encoding of object slippage

Slip Detection Strategies for Automatic Grasping in Prosthetic Hands

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

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