Objekt-Manipulation und Steuerung der Greifkraft durch Verwendung von Taktilen Sensoren

This dissertation describes a new type of tactile sensor and an improved version of the dynamic tactile sensing approach that can provide a regularly updated and accurate estimate of minimum applied forces for use in the control of gripper manipulation. The pre-slip sensing algorithm is proposed and implemented into two-finger robot gripper. An algorithm that can discriminate between types of contact surface and recognize objects at the contact stage is also proposed. A technique for recognizing objects using tactile sensor arrays, and a method based on the quadric surface parameter for classifying grasped objects is described. Tactile arrays can recognize surface types on contact, making it possible for a tactile system to recognize translation, rotation, and scaling of an object independently.Diese Dissertation beschreibt eine neue Art von taktilen Sensoren und einen verbesserten Ansatz zur dynamischen Erfassung von taktilen daten, der in regelmäßigen Zeitabständen eine genaue Bewertung der minimalen Greifkraft liefert, die zur Steuerung des Greifers nötig ist. Ein Berechnungsverfahren zur Voraussage des Schlupfs, das in einen Zwei-Finger-Greifarm eines Roboters eingebaut wurde, wird vorgestellt. Auch ein Algorithmus zur Unterscheidung von verschiedenen Oberflächenarten und zur Erkennung von Objektformen bei der Berührung wird vorgestellt. Ein Verfahren zur Objekterkennung mit Hilfe einer Matrix aus taktilen Sensoren und eine Methode zur Klassifikation ergriffener Objekte, basierend auf den Daten einer rechteckigen Oberfläche, werden beschrieben. Mit Hilfe dieser Matrix können unter schiedliche Arten von Oberflächen bei Berührung erkannt werden, was es für das Tastsystem möglich macht, Verschiebung, Drehung und Größe eines Objektes unabhängig voneinander zu erkennen

Methods and Sensors for Slip Detection in Robotics: A Survey

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

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

A high speed sensor system for tactile interaction research

Schürmann C. A high speed sensor system for tactile interaction research. Bielefeld: Bielefeld University Library; 2013.In this work we will describe and implement the first tactile sensor system that combines the properties of modularity with a very high sensing speed, a high sensitivity and a high spatial resolution. This unique combination of features enables researchers to develop novel applications and makes it possible to replace task specific tactile sensors with a single system. The very high sensing speed of the system allows for slip detection during robot grasping. And as all our sensor cells are sampled with the same high frequency, our system can even enable the slip detection for multiple contact points at the same time. This high speed was made possible through the development of a highly integrated parallel sensor sampling architecture. The modularity of the system allows it to be employed in a multitude of applications. Tactile sensitive surfaces of various dimensions can be easily realized through a very simple ’plug and use’ principle without the need for software configuration by the user. This was made possible by developing a new bus system that allows the relative localization of the participants. Our system can be used to create tactile sensitive table surfaces with a large amount of sensor cells and due to its high speed design still provide for real time frame rates. The flexibility and high performance of the system enabled us to develop a tactile sensitive object that allows the continuous high speed monitoring of human finger forces. For this we solved the problem of integrating the tactile sensors to allow free movement of the object, while maintaining a constant high rate of data capture and realizing a low latency synchronization to external devices. The high sensitivity of the system was made possible through technical innovation in the state of the art of resistive based tactile sensors. We did so by creating an optimized sensor cell shape and investigating the behavior of different sensor materials. The knowledge gained in this process was further used to advance the existing method of sensor normalization into a real time method. We will present a range of tactile interaction scenarios that have been realized with the tactile sensor system named Myrmex. These scenarios include the investigating of human grasp force control during a pick and place task, a tactile table for integration into an intelligent household and a tactile table for the manipulation of virtual clay as a form of finger training. In addition we will present a selection of scenarios where the Myrmex system was employed by other researchers, as in using the sensor modules as (large) tactile fingertips on robot arms to implement tactile servoing or slip detection during object grasping. The system has also been used to study human finger forces as well as investigating novel methods for prosthesis control. The positive results from all the scenarios support our conclusion that the developed Myrmex system is a very valuable and reliable tool for the research of tactile interactions

Signal and Information Processing Methods for Embedded Robotic Tactile Sensing Systems

The human skin has several sensors with different properties and responses that are able to detect stimuli resulting from mechanical stimulations. Pressure sensors are the most important type of receptors for the exploration and manipulation of objects. In the last decades, smart tactile sensing based on different sensing techniques have been developed as their application in robotics and prosthetics is considered of huge interest, mainly driven by the prospect of autonomous and intelligent robots that can interact with the environment. However, regarding object properties estimation on robots, hardness detection is still a major limitation due to the lack of techniques to estimate it. Furthermore, finding processing methods that can interpret the measured information from multiple sensors and extract relevant information is a Challenging task. Moreover, embedding processing methods and machine learning algorithms in robotic applications to extract meaningful information such as object properties from tactile data is an ongoing challenge, which is controlled by the device constraints (power constraint, memory constraints, etc.), the computational complexity of the processing and machine learning algorithms, the application requirements (real-time operations, high prediction performance). In this dissertation, we focus on the design and implementation of pre-processing methods and machine learning algorithms to handle the aforementioned challenges for a tactile sensing system in robotic application. First, we propose a tactile sensing system for robotic application. Then we present efficient preprocessing and feature extraction methods for our tactile sensors. Then we propose a learning strategy to reduce the computational cost of our processing unit in object classification using sensorized Baxter robot. Finally, we present a real-time robotic tactile sensing system for hardness classification on a resource-constrained devices. The first study represents a further assessment of the sensing system that is based on the PVDF sensors and the interface electronics developed in our lab. In particular, first, it presents the development of a skin patch (multilayer structure) that allows us to use the sensors in several applications such as robotic hand/grippers. Second, it shows the characterization of the developed skin patch. Third, it validates the sensing system. Moreover, we designed a filter to remove noise and detect touch. The experimental assessment demonstrated that the developed skin patch and the interface electronics indeed can detect different touch patterns and stimulus waveforms. Moreover, the results of the experiments defined the frequency range of interest and the response of the system to realistic interactions with the sensing system to grasp and release events. In the next study, we presented an easy integration of our tactile sensing system into Baxter gripper. Computationally efficient pre-processing techniques were designed to filter the signal and extract relevant information from multiple sensor signals, in addition to feature extraction methods. These processing methods aim in turn to reduce also the computational complexity of machine learning algorithms utilized for object classification. The proposed system and processing strategy were evaluated on object classification application by integrating our system into the gripper and we collected data by grasping multiple objects. We further proposed a learning strategy to accomplish a trade-off between the generalization accuracy and the computational cost of the whole processing unit. The proposed pre-processing and feature extraction techniques together with the learning strategy have led to models with extremely low complexity and very high generalization accuracy. Moreover, the support vector machine achieved the best trade-off between accuracy and computational cost on tactile data from our sensors. Finally, we presented the development and implementation on the edge of a real–time tactile sensing system for hardness classification on Baxter robot based on machine and deep learning algorithms. We developed and implemented in plain C a set of functions that provide the fundamental layer functionalities of the Machine learning and Deep Learning models (ML and DL), along with the pre–processing methods to extract the features and normalize the data. The models can be deployed to any device that supports C code since it does not rely on any of the existing libraries. Shallow ML/DL algorithms for the deployment on resource–constrained devices are designed. To evaluate our work, we collected data by grasping objects of different hardness and shape. Two classification problems were addressed: 5 levels of hardness classified on the same objects’ shape, and 5 levels of hardness classified on two different objects’ shape. Furthermore, optimization techniques were employed. The models and pre–processing were implemented on a resource constrained device, where we assessed the performance of the system in terms of accuracy, memory footprint, time latency, and energy consumption. We achieved for both classification problems a real-time inference (< 0.08 ms), low power consumption (i.e., 3.35 μJ), extremely small models (i.e., 1576 Byte), and high accuracy (above 98%)

Distributed Sensing and Stimulation Systems Towards Sense of Touch Restoration in Prosthetics

Modern prostheses aim at restoring the functional and aesthetic characteristics of the lost limb. To foster prosthesis embodiment and functionality, it is necessary to restitute both volitional control and sensory feedback. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing high-fidelity spatial information. To provide this type of feedback in prosthetics, it is necessary to sense tactile information from artificial skin placed on the prosthesis and transmit tactile feedback above the amputation in order to map the interaction between the prosthesis and the environment. This thesis proposes the integration of distributed sensing systems (e-skin) to acquire tactile sensation, and non-invasive multichannel electrotactile feedback and virtual reality to deliver high-bandwidth information to the user. Its core focus addresses the development and testing of close-loop sensory feedback human-machine interface, based on the latest distributed sensing and stimulation techniques for restoring the sense of touch in prosthetics. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives and the used methodology and contributions; as well as three studies distributed over stimulation system level and sensing system level. The first study presents the development of close-loop compensatory tracking system to evaluate the usability and effectiveness of electrotactile sensory feedback in enabling real-time close-loop control in prosthetics. It examines and compares the subject\u2019s adaptive performance and tolerance to random latencies while performing the dynamic control task (i.e. position control) and simultaneously receiving either visual feedback or electrotactile feedback for communicating the momentary tracking error. Moreover, it reported the minimum time delay needed for an abrupt impairment of users\u2019 performance. The experimental results have shown that electrotactile feedback performance is less prone to changes with longer delays. However, visual feedback drops faster than electrotactile with increased time delays. This is a good indication for the effectiveness of electrotactile feedback in enabling close- loop control in prosthetics, since some delays are inevitable. The second study describes the development of a novel non-invasive compact multichannel interface for electrotactile feedback, containing 24 pads electrode matrix, with fully programmable stimulation unit, that investigates the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the electrode matrix. Furthermore, it designed a novel dual parameter -modulation (interleaved frequency and intensity) and compared it to conventional stimulation (same frequency for all pads). In addition and for the first time, it compared the electrotactile stimulation to mechanical stimulation. More, it exposes the integration of virtual prosthesis with the developed system in order to achieve better user experience and object manipulation through mapping the acquired real-time collected tactile data and feedback it simultaneously to the user. The experimental results demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. Furthermore, it showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. The third study presents the realization of a novel, flexible, screen- printed e-skin based on P(VDF-TrFE) piezoelectric polymers, that would cover the fingertips and the palm of the prosthetic hand (particularly the Michelangelo hand by Ottobock) and an assistive sensorized glove for stroke patients. Moreover, it developed a new validation methodology to examine the sensors behavior while being solicited. The characterization results showed compatibility between the expected (modeled) behavior of the electrical response of each sensor to measured mechanical (normal) force at the skin surface, which in turn proved the combination of both fabrication and assembly processes was successful. This paves the way to define a practical, simplified and reproducible characterization protocol for e-skin patches In conclusion, by adopting innovative methodologies in sensing and stimulation systems, this thesis advances the overall development of close-loop sensory feedback human-machine interface used for restoration of sense of touch in prosthetics. Moreover, this research could lead to high-bandwidth high-fidelity transmission of tactile information for modern dexterous prostheses that could ameliorate the end user experience and facilitate it acceptance in the daily life

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

Development of highly sensitive multimodal tactile sensor

The sense of touch is crucial for interpreting exteroceptive stimuli, and for moderating physical interactions with one’s environment during object grasping and manipulation tasks. For years, tactile researchers have sought a method that will allow robots to achieve the same tactile sensing capabilities as humans, but the solution has remained elusive. This is a problem for people in the medical and robotics communities, as prosthetic and robotic limbs provide little or no force feedback during contact with objects. During object manipulation tasks, the inability to control the force (applied by the prosthetic or robotic hand to the object) frequently results in damage to the object. Moreover, amputees must compensate for the lack of tactility by paying continuous visual attention to the task at hand, making even the simplest task a frustrating and time-consuming endeavor. We believe that these challenges of object manipulation might best be addressed by a closed feedback loop with a tactile sensory system that is capable of detecting multiple stimuli. To this end, the goal of our research is the development of a tactile sensor that mimics the human sensory apparatus as closely as possible. Thus far, tactile sensors have been unable to match the human sensory apparatus in terms of simultaneous multimodality, high resolution, and broad sensitivity. In particular, previous sensors have typically been able to sense either a wide range of forces, or very low forces, but never both at the same time; and they are designed for either static or dynamic sensing, rather than multimodality. These restrictions have left them unsuited to the needs of robotic applications. Capacitance-based sensors represent the most promising approach, but they too must overcome many limitations. Although recent innovations in the touch screen industry have resolved the issue of processing complexity, through the replacement of clunky processing circuits with new integrated circuits (ICs), most capacitive sensors still remain limited by hysteresis and narrow ranges of sensitivity, due to the properties of their dielectrics. In this thesis, we present the design of a new capacitive tactile sensor that is capable of making highly accurate measurements at low force levels, while also being sensitive to a wide range of forces. Our sensor is not limited to the detection of either low forces or broad sensitivity, because the improved soft dielectric that we have constructed allows it to do both at the same time. To construct the base of the dielectric, we used a geometrically modified silicone material. To create this material, we used a soft-lithography process to construct microfeatures that enhance the silicone’s compressibility under pressure. Moreover, the silicone was doped with high-permittivity ceramic nanoparticles, thereby enhancing the capacitive response of the sensor. Our dielectric features a two-stage microstructure, which makes it very sensitive to low forces, while still able to measure a wide range of forces. Despite these steps, and the complexity of the dielectric’s structure, we were still able to fabricate the dielectric using a relatively simple process. In addition, our sensor is not limited to either static or dynamic sensing; unlike previous sensors, it is capable of doing both simultaneously. This multimodality allows our sensor to detect fluctuating forces, even at very low force levels. Whereas past researchers have used separate technologies for static and dynamic sensing, our dynamic sensing unit is formed with same capacitive technology as the static one. This was possible because of the high sensitivity of our dielectric. We used the entire surface area effectively, by integrating the single dynamic sensing taxel on the same layer as the static sensing taxels. Essentially, the dynamic taxel takes the shape of the lines of a grid, filling in the spaces between the individual static taxels. For further optimization, the geometry of the dynamic taxel has been redesigned by fringing miniature traces of the dynamic taxel within the static taxels. In this way, the entire surface of the sensor is sensitive to both dynamic and static events. While this design slightly reduces the area that is covered by the static taxels, the trade-off is justified, as the capacitive behavior is boosted by the edge effect of the capacitor. The fusion of an innovative dielectric with a capacitive sensing IC has produced a highly sensitive tactile sensor that meets our goals regarding resolution, noise immunity, and overall performance. It is sensitive to forces ranging from 1 mN to 15 N. We verified the functionality of our sensor by mounting it on several of the most popular mechanical hands. Our grasp assessment experiments delivered promising results, and showed how our sensor might be further refined so that it can be used to accurately estimate the outcome of an attempted grasp. In future, we believe that combining an advanced robotic hand with the sensor we have developed will allow us to meet the demand for human-like tactile sensing abilities