Experimental Study on Human Arm Reaching with and without a Reduced Mobility for Applications in Medical Human-Interactive Robotics

Along with increasing advances in robotic technologies, there are now significant efforts under way to improve the quality of life especially those with physical disabilities or impairments. Control of such medical human-interactive robotics (HIR) involves complications in its design and control due to uncertain human factors. This dissertation makes its efforts to resolve three main challenges of an advanced HIR controller development: 1) detecting the operator’s motion intent, 2) understanding human motor behavior from the robotic perspective, and 3) generating reference motion for the HIR. Our interests in such challenges are limited to the point-to-point reaching of the human arm for applications of their solutions in the control of rehabilitation exoskeletons, therapeutic haptic devices, and prosthetic arms. In the context of human motion intent detection, a mobile motion capture system (MCS) enhanced with myoprocessors is developed to capture kinematics and dynamics of human arm in reaching movements. The developed MCS adopts wireless IMU (inertial measurement unit) sensors to capture ADL (activities of daily life) motions in the real-life environment. In addition, measured muscle activation patterns from selected muscle groups are converted into muscular force values by myoprocessors. This allows a reliable motion intent detection by quantify one of the most frequently used driving signal of the HIR, EMG (electromyography), in a standardized way. In order to understand the human motor behavior from the robotic viewpoint, a computational model on reaching is required. Since such model can be constituted by experimental observations, this dissertation look into invariant motion features of reaching with and without elbow constraint condition to establish a foundation of the computational model. The HIR should generate its reference motions by reflecting motor behavior of the natural human reaching. Though the accurate approximation of such behavior is critical, we also need to take into account the computational cost, especially for real-time applications such as the HIR control. In this manner, a higher order kinematic synthesis of mechanical linkage systems is adopted to approximate natural human hand profiles. Finally, a novel control concept of a myo-prosthetic arm is proposed as an application of all findings and efforts made in this dissertation

Challenges of continuum robots in clinical context: a review

With the maturity of surgical robotic systems based on traditional rigid-link principles, the rate of progress slowed as limits of size and controllable degrees of freedom were reached. Continuum robots came with the potential to deliver a step change in the next generation of medical devices, by providing better access, safer interactions and making new procedures possible. Over the last few years, several continuum robotic systems have been launched commercially and have been increasingly adopted in hospitals. Despite the clear progress achieved, continuum robots still suffer from design complexity hindering their dexterity and scalability. Recent advances in actuation methods have looked to address this issue, offering alternatives to commonly employed approaches. Additionally, continuum structures introduce significant complexity in modelling, sensing, control and fabrication; topics which are of particular focus in the robotics community. It is, therefore, the aim of the presented work to highlight the pertinent areas of active research and to discuss the challenges to be addressed before the potential of continuum robots as medical devices may be fully realised

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Continuum Mechanical Models for Design and Characterization of Soft Robots

The emergence of ``soft'' robots, whose bodies are made from stretchable materials, has fundamentally changed the way we design and construct robotic systems. Demonstrations and research show that soft robotic systems can be useful in rehabilitation, medical devices, agriculture, manufacturing and home assistance. Increasing need for collaborative, safe robotic devices have combined with technological advances to create a compelling development landscape for soft robots. However, soft robots are not yet present in medical and rehabilitative devices, agriculture, our homes, and many other human-collaborative and human-interactive applications. This gap between promise and practical implementation exists because foundational theories and techniques that exist in rigid robotics have not yet been developed for soft robots. Theories in traditional robotics rely on rigid body displacements via discrete joints and discrete actuators, while in soft robots, kinematic and actuation functions are blended, leading to nonlinear, continuous deformations rather than rigid body motion. This dissertation addresses the need for foundational techniques using continuum mechanics. Three core questions regarding the use of continuum mechanical models in soft robotics are explored: (1) whether or not continuum mechanical models can describe existing soft actuators, (2) which physical phenomena need to be incorporated into continuum mechanical models for their use in a soft robotics context, and (3) how understanding on continuum mechanical phenomena may form bases for novel soft robot architectures. Theoretical modeling, experimentation, and design prototyping tools are used to explore Fiber-Reinforced Elastomeric Enclosures (FREEs), an often-used soft actuator, and to develop novel soft robot architectures based on auxetic behavior. This dissertation develops a continuum mechanical model for end loading on FREEs. This model connects a FREE’s actuation pressure and kinematic configuration to its end loads by considering stiffness of its elastomer and fiber reinforcement. The model is validated against a large experimental data set and compared to other FREE models used by roboticists. It is shown that the model can describe the FREE’s loading in a generalizable manner, but that it is bounded in its peak performance. Such a model can provide the novel function of evaluating the performance of FREE designs under high loading without the costs of building and testing prototypes. This dissertation further explores the influence viscoelasticity, an inherent property of soft polymers, on end loading of FREEs. The viscoelastic model developed can inform soft roboticists wishing to exploit or avoid hysteresis and force reversal. The final section of the dissertations explores two contrasting styles of auxetic metamaterials for their uses in soft robotic actuation. The first metamaterial architecture is composed of beams with distributed compliance, which are placed antagonistic configurations on a variety of surfaces, giving ride to shape morphing behavior. The second metamaterial architecture studied is a ``kirigami’’ sheet with an orthogonal cut pattern, utilizing lumped compliance and strain hardening to permanently deploy from a compact shape to a functional one. This dissertation lays the foundation for design of soft robots by robust physical models, reducing the need for physical prototypes and trial-and-error approaches. The work presented provides tools for systematic exploration of FREEs under loading in a wide range of configurations. The work further develops new concepts for soft actuators based on continuum mechanical modeling of auxetic metamaterials. The work presented expands the available tools for design and development of soft robotic systems, and the available architectures for soft robot actuation.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163236/1/asedal_1.pd

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

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

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application