Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Adaptive and reconfigurable robotic gripper hands with a meso-scale gripping range

Grippers and robotic hands are essential and important end-effectors of robotic manipulators. Developing a gripper hand that can grasp a large variety of objects precisely and stably is still an aspiration even though research in this area has been carried out for several decades. This thesis provides a development approach and a series of gripper hands which can bridge the gap between micro-gripper and macro-gripper by extending the gripping range to the mesoscopic scale (meso-scale). Reconfigurable topology and variable mobility of the design offer versatility and adaptability for the changing environment and demands. By investigating human grasping behaviours and the unique structures of human hand, a CFB-based finger joint for anthropomorphic finger is developed to mimic a human finger with a large grasping range. The centrodes of CFB mechanism are explored and a contact-aided CFB mechanism is developed to increase stiffness of finger joints. An integrated gripper structure comprising cross four-bar (CFB) and remote-centre-of-motion (RCM) mechanisms is developed to mimic key functionalities of human hand. Kinematics and kinetostatic analyses of the CFB mechanism for multimode gripping are conducted to achieve passive-adjusting motion. A novel RCM-based finger with angular, parallel and underactuated motion is invented. Kinematics and stable gripping analyses of the RCM-based multi-motion finger are also investigated. The integrated design with CFB and RCM mechanisms provides a novel concept of a multi-mode gripper that aims to tackle the challenge of changing over for various sizes of objects gripping in mesoscopic scale range. Based on the novel designed mechanisms and design philosophy, a class of gripper hands in terms of adaptive meso-grippers, power-precision grippers and reconfigurable hands are developed. The novel features of the gripper hands are one degree of freedom (DoF), self-adaptive, reconfigurable and multi-mode. Prototypes are manufactured by 3D printing and the grasping abilities are tested to verify the design approach.EPSR

A Methodology for the Design of Robotic Hands with Multiple Fingers

This paper presents a methodology that has been applied for a design process of anthropomorphic hands with multiple fingers. Biomechanical characteristics of human hand have been analysed so that ergonomic and anthropometric aspects have been used as fundamental references for obtaining grasping mechanisms. A kinematic analysis has been proposed to define the requirements for designing grasping functions. Selection of materials and actuators has been discussed too. This topic has been based on previous experiences with prototypes that have been developed at the Laboratory of Robotics and Mechatronics (LARM) of the University of Cassino. An example of the application of the proposed method has been presented for the design of a first prototype of LARM Hand

Elastic Inflatable Actuators for Soft Robotic Applications

The 20th century’s robotic systems have been made out of stiff materials and much of the developments in the field have pursued ever more accurate and dynamic robots which thrive in industrial automation settings and will probably continue to do so for many decades to come. However, the 21st century’s robotic legacy may very well become that of soft robots. This emerging domain is characterized by continuous soft structures that simultaneously fulfil the role of robotic link and robotic actuator, where prime focus is on design and fabrication of the robotic hardware instead of software control to achieve a desired operation. These robots are anticipated to take a prominent role in delicate tasks where classic robots fail, such as in minimally invasive surgery, active prosthetics and automation tasks involving delicate irregular objects. Central to the development of these robots is the fabrication of soft actuators to generate movement. This paper reviews a particularly attractive type of soft actuators that are driven by pressurized fluids. These actuators have recently gained substantial traction on the one hand due to the technology push from better simulation tools and new manufacturing technologies including soft-lithography and additive manufacturing, and on the other hand by a market pull from the applications listed above. This paper provides an overview of the different advanced soft actuator configurations, their design, fabrication and applications.This research is supported by the Fund for Scientific Research-Flanders (FWO), and the European Research Council (ERC starting grant HIENA)

Elastic Inflatable Actuators for Soft Robotic Applications

The 20th century’s robotic systems have been made out of stiff materials and much of the developments in the field have pursued ever more accurate and dynamic robots which thrive in industrial automation settings and will probably continue to do so for many decades to come. However, the 21st century’s robotic legacy may very well become that of soft robots. This emerging domain is characterized by continuous soft structures that simultaneously fulfil the role of robotic link and robotic actuator, where prime focus is on design and fabrication of the robotic hardware instead of software control to achieve a desired operation. These robots are anticipated to take a prominent role in delicate tasks where classic robots fail, such as in minimally invasive surgery, active prosthetics and automation tasks involving delicate irregular objects. Central to the development of these robots is the fabrication of soft actuators to generate movement. This paper reviews a particularly attractive type of soft actuators that are driven by pressurized fluids. These actuators have recently gained substantial traction on the one hand due to the technology push from better simulation tools and new manufacturing technologies including soft-lithography and additive manufacturing, and on the other hand by a market pull from the applications listed above. This paper provides an overview of the different advanced soft actuator configurations, their design, fabrication and applications.This research is supported by the Fund for Scientific Research-Flanders (FWO), and the European Research Council (ERC starting grant HIENA)

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

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

Design of a pneumatic soft robotic actuator using model-based optimization

In this thesis, the design and optimization process of a novel soft intelligent modular pad (IntelliPad) for the purpose of pressure injury prevention is presented. The structure of the IntelliPad consists of multiple individual multi-chamber soft pneumatic-driven actuators that use pressurized air and vacuum. Each actuator is able to provide both vertical and horizontal motions that can be controlled independently. An analytical modeling approach using multiple cantilever beams and virtual springs connected in a closed formed structure was developed to analyze the mechanical performance of the actuator. The analytical approach was validated by a finite element analysis. For optimizing the actuator\u27s mechanical performance, firefly algorithm and deep reinforcement learning-based design optimization frameworks were developed with the purpose of maximizing the horizontal motion of the top surface of the actuators, while minimizing its corresponding effect on the vertical motion. Four optimized designs were fabricated. The actuators were tested and validated experimentally to demonstrate their required mechanical performance in order to regulate normal and shear stresses at the skin-pad interface for pressure injury prevention applications