Design and Development of Sensor Integrated Robotic Hand

Most of the automated systems using robots as agents do use few sensors according to the need. However, there are situations where the tasks carried out by the end-effector, or for that matter by the robot hand needs multiple sensors. The hand, to make the best use of these sensors, and behave autonomously, requires a set of appropriate types of sensors which could be integrated in proper manners. The present research work aims at developing a sensor integrated robot hand that can collect information related to the assigned tasks, assimilate there correctly and then do task action as appropriate. The process of development involves selection of sensors of right types and of right specification, locating then at proper places in the hand, checking their functionality individually and calibrating them for the envisaged process. Since the sensors need to be integrated so that they perform in the desired manner collectively, an integration platform is created using NI PXIe-1082. A set of algorithm is developed for achieving the integrated model. The entire process is first modelled and simulated off line for possible modification in order to ensure that all the sensors do contribute towards the autonomy of the hand for desired activity. This work also involves design of a two-fingered gripper. The design is made in such a way that it is capable of carrying out the desired tasks and can accommodate all the sensors within its fold. The developed sensor integrated hand has been put to work and its performance test has been carried out. This hand can be very useful for part assembly work in industries for any shape of part with a limit on the size of the part in mind. The broad aim is to design, model simulate and develop an advanced robotic hand. Sensors for pick up contacts pressure, force, torque, position, surface profile shape using suitable sensing elements in a robot hand are to be introduced. The hand is a complex structure with large number of degrees of freedom and has multiple sensing capabilities apart from the associated sensing assistance from other organs. The present work is envisaged to add multiple sensors to a two-fingered robotic hand having motion capabilities and constraints similar to the human hand. There has been a good amount of research and development in this field during the last two decades a lot remains to be explored and achieved. The objective of the proposed work is to design, simulate and develop a sensor integrated robotic hand. Its potential applications can be proposed for industrial environments and in healthcare field. The industrial applications include electronic assembly tasks, lighter inspection tasks, etc. Application in healthcare could be in the areas of rehabilitation and assistive techniques. The work also aims to establish the requirement of the robotic hand for the target application areas, to identify the suitable kinds and model of sensors that can be integrated on hand control system. Functioning of motors in the robotic hand and integration of appropriate sensors for the desired motion is explained for the control of the various elements of the hand. Additional sensors, capable of collecting external information and information about the object for manipulation is explored. Processes are designed using various software and hardware tools such as mathematical computation MATLAB, OpenCV library and LabVIEW 2013 DAQ system as applicable, validated theoretically and finally implemented to develop an intelligent robotic hand. The multiple smart sensors are installed on a standard six degree-of-freedom industrial robot KAWASAKI RS06L articulated manipulator, with the two-finger pneumatic SHUNK robotic hand or designed prototype and robot control programs are integrated in such a manner that allows easy application of grasping in an industrial pick-and-place operation where the characteristics of the object can vary or are unknown. The effectiveness of the actual recommended structure is usually proven simply by experiments using calibration involving sensors and manipulator. The dissertation concludes with a summary of the contribution and the scope of further work

Robotic manipulator inspired by human fingers based on tendon-driven soft grasping

Die menschliche Hand ist in der Lage, verschiedene Greif- und Manipulationsaufgaben auszuführen und kann als einer der geschicktesten und vielseitigsten Effektoren angesehen werden. In dieser Arbeit wurde ein Soft Robotic-Greifer entwickelt, der auf den Erkenntnissen aus der Literatur zur Taxonomie der menschlichen Greiffähigkeiten und den biomechanischen Synergien der menschlichen Hand basiert. Im Bereich der Roboterhände sind sehnengetriebene, unteraktuierte Strukturen weit verbreitet. Inspiriert von der Anatomie der menschlichen Hand, bieten sie durch ihre Flexibilität passive Adaptivität und Robustheit. Es wurde ein Sensorsystem implementiert, bestehend aus Force Sensing Resistors (FSRs), Biegungssensoren und einem Stromsensor, wodurch das System charakterisiert werden kann. Die Kraftsensoren wurden in die Fingerkuppen integriert. In Anlehnung an die menschliche Haut wurden Abgüsse aus Silikonkautschuk an den Fingerballen verwendet. Diese versprechen eine erhöhte Reibung und bessere Adaptivität zum gegriffenen Objekt. Um den entwickelten Greifer zu evaluieren, wurden erste Tests durchgeführt. Zunächst wurde die Funktionalität der Sensoren, wie z.B. der als FSRs ausgewählten Kraftsensoren, getestet. Im weiteren Verlauf wurden die Greiffähigkeiten des Greifers durch Manipulation verschiedener Objekte getestet. Basierend auf den Erkenntnissen aus den praktischen Versuchen kann festgestellt werden, dass der entwickelte Greifer ein hohes Maß an Geschicklichkeit aufweist. Auch die Adaptivität ist dank der verwendeten mechanischen Komponenten gewährleistet. Mittels der Sensorik ist es möglich, den Greifprozess zu kontrollieren. Die Ergebnisse zeigen aber auch, dass z. B. die interne Systemreibung die Verlustleistung des Systems stark beeinflusst.The human hand is able to perform various grasping and manipulation tasks, and can be seen as one of the most dexterous and versatile effectors known. The prehensile capabilities of the hand have already been analyzed, categorized and summarized in a taxonomy in numerous studies. In addition to the taxonomies, research on the biomechanical synergies of the human hand led to the following conceptions: The adduction/abduction movement is independent of the flexion/extension movement. Furthermore, the thumb is rather independent in its mobility from the other fingers, while those move synchronously within their corresponding joints. Lastly, the consideration of the synergies provides that the proximal and distal interphalangeal joints of a human finger are more intensely coordinated than those of the metacarpal joints. In this work, a soft robotic gripper was developed based on the knowledge from the literature on the taxonomy of human gripping abilities and the biomechanical synergies of the human hand. In the domain of robotic hands, tendon-driven underactuated structures are widely used. Inspired by the tensegrity structure of the human hand, they offer passive adaptivity and robustness through their flexibility. A sensor system was implemented, consisting of FSRs, flex sensors and a current sensor, thus the system parameters can be characterized continously. The force sensors were integrated into the fingertips. Molds of silicone rubber were used as finger pads to provide higher friction and better adaptivity to the grasped object on the contact areas of the finger, to mimic human skin. Initial tests were carried out to evaluate the gripper. First, the functionality of the sensors, such as the force sensors selected as FSRs, was tested. In the further course, the gripping capabilities of the gripper were tested by manipulation of various different objects. Based on the findings from the practical experiments, it may be stated that the gripper has a high degree of dexterity. Thanks to the mechanical components used, adaptivity is guaranteed as well. By means of the sensor system it is possible to control the gripping processes. However, the results also showed that, for example, the internal system friction dominates the system’s power dissipation

マルチ スケール キノウ ヲ ユウスル コウソク ジドウ マイクロ マニピュレーション システム

Ebubekir Avci, Chanh-Nghiem Nguyen, Kenichi Ohara, Yasushi Mae, Tatsuo Arai, Analysis and suppression of residual vibration in microhand for high-speed single-cell manipulation, International Journal of Mechatronics and Automation, 2013-Vol.3, No.2, pp.110-11

Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatū, New Zealand

All copyrighted Figures have been removed but may be accessed via their source cited in their respective captions.Advanced human-computer interaction (HCI) or human-machine interaction (HMI) aims to help humans interact with computers smartly. Biosignal-based technology is one of the most promising approaches in developing intelligent HCI systems. As a means of convenient and non-invasive biosignal-based intelligent control, myoelectric control identifies human movement intentions from electromyogram (EMG) signals recorded on muscles to realise intelligent control of robotic systems. Although the history of myoelectric control research has been more than half a century, commercial myoelectric-controlled devices are still mostly based on those early threshold-based methods. The emerging pattern recognition-based myoelectric control has remained an active research topic in laboratories because of insufficient reliability and robustness. This research focuses on pattern recognition-based myoelectric control. Up to now, most of effort in pattern recognition-based myoelectric control research has been invested in improving EMG pattern classification accuracy. However, high classification accuracy cannot directly lead to high controllability and usability for EMG-driven systems. This suggests that a complete system that is composed of relevant modules, including EMG acquisition, pattern recognition-based gesture discrimination, output equipment and its controller, is desirable and helpful as a developing and validating platform that is able to closely emulate real-world situations to promote research in myoelectric control. This research aims at investigating feasible and effective EMG signal processing and pattern recognition methods to extract useful information contained in EMG signals to establish an intelligent, compact and economical biosignal-based robotic control system. The research work includes in-depth study on existing pattern recognition-based methodologies, investigation on effective EMG signal capturing and data processing, EMG-based control system development, and anthropomorphic robotic hand design. The contributions of this research are mainly in following three aspects: Developed precision electronic surface EMG (sEMG) acquisition methods that are able to collect high quality sEMG signals. The first method was designed in a single-ended signalling manner by using monolithic instrumentation amplifiers to determine and evaluate the analog sEMG signal processing chain architecture and circuit parameters. This method was then evolved into a fully differential analog sEMG detection and collection method that uses common commercial electronic components to implement all analog sEMG amplification and filtering stages in a fully differential way. The proposed fully differential sEMG detection and collection method is capable of offering a higher signal-to-noise ratio in noisy environments than the single-ended method by making full use of inherent common-mode noise rejection capability of balanced signalling. To the best of my knowledge, the literature study has not found similar methods that implement the entire analog sEMG amplification and filtering chain in a fully differential way by using common commercial electronic components. Investigated and developed a reliable EMG pattern recognition-based real-time gesture discrimination approach. Necessary functional modules for real-time gesture discrimination were identified and implemented using appropriate algorithms. Special attention was paid to the investigation and comparison of representative features and classifiers for improving accuracy and robustness. A novel EMG feature set was proposed to improve the performance of EMG pattern recognition. Designed an anthropomorphic robotic hand construction methodology for myoelectric control validation on a physical platform similar to in real-world situations. The natural anatomical structure of the human hand was imitated to kinematically model the robotic hand. The proposed robotic hand is a highly underactuated mechanism, featuring 14 degrees of freedom and three degrees of actuation. This research carried out an in-depth investigation into EMG data acquisition and EMG signal pattern recognition. A series of experiments were conducted in EMG signal processing and system development. The final myoelectric-controlled robotic hand system and the system testing confirmed the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture discrimination. To verify and demonstrate the proposed myoelectric control system, real-time tests were conducted onto the anthropomorphic prototype robotic hand. Currently, the system is able to identify five patterns in real time, including hand open, hand close, wrist flexion, wrist extension and the rest state. With more motion patterns added in, this system has the potential to identify more hand movements. The research has generated a few journal and international conference publications

Adaptive Human Control Gains During Precision Grip

Eight human test subjects attempted to track a desired position trajectory with an instrumented manipulandum (MN). The test subjects used the MN with three different levels of stiffness. A transfer function was developed to represent the human application of a precision grip from the data when the test subjects initially displaced the MN so as to learn the position mapping from the MN onto the display. Another transfer function was formed from the data of the remainder of the experiments, after significant displacement of the MN occurred. Both of these transfer functions accurately modelled the system dynamics for a portion of the experiments, but neither was accurate for the duration of the experiments because the human grip dynamics changed while learning the position mapping. Thus, an adaptive system model was developed to describe the learning process of the human test subjects as they displaced the MN in order to gain knowledge of the position mapping. The adaptive system model was subsequently validated following comparison with the human test subject data. An examination of the average absolute error between the position predicted by the adaptive model and the actual experimental data yielded an overall average error of 0.34mm for all three levels of stiffness

Tactile to vibrotactile sensory feedback interface for prosthetic hand users

The motivation of this research work is to provide a sense of embodiment to prosthetic users by supplementing their devices with sensory feedback to the residual upper arm. This sensory feedback replicates the tactile sensory system of glabrous skin that covers palm and flexor surfaces of fingers. In this work, we produced vibration patterns that will be perceived at the upper arm, according to signals obtained by a prosthetic finger when sliding across fabricated textured surfaces. This was done by transforming the signals to ‘on’ and ‘off’ pulses in the LabView environment and then forwarded to a data acquisition board to provide voltage signals to a vibration actuator. We implemented a novel frequency measurement procedure to maintain a vibration frequency of 250 Hz, which is the optimum frequency of the mechanoreceptors underneath the skin of the upper arm in detecting vibration. The outcome from this research work leads to optimistic possibility that a touch sensation that was previously lost could be restored to different parts of the body. This undoubtedly will increase users’ acceptance of the device as a part of their body due to its ‘lifelike’ quality