Elbow exoskeleton mechanism for multistage poststroke rehabilitation.

More than three million people are suffering from stroke in England. The process of post-stroke rehabilitation consists of a series of biomechanical exercises- controlled joint movement in acute phase; external assistance in the mid phase; and variable levels of resistance in the last phase. Post-stroke rehabilitation performed by physiotherapist has many limitations including cost, time, repeatability and intensity of exercises. Although a large variety of arm exoskeletons have been developed in the last two decades to substitute the conventional exercises provided by physiotherapist, most of these systems have limitations with structural configuration, sensory data acquisition and control architecture. It is still difficult to facilitate multistage post-stroke rehabilitation to patients sited around hospital bed without expert intervention. To support this, a framework for elbow exoskeleton has been developed that is portable and has the potential to offer all three types of exercises (external force, assistive and resistive) in a single structure. The design enhances torque to weight ratio compared to joint based actuation systems. The structural lengths of the exoskeleton are determined based on the mean anthropometric parameters of healthy users and the lengths of upperarm and forearm are determined to fit a wide range of users. The operation of the exoskeleton is divided into three regions where each type of exercise can be served in a specific way depending on the requirements of users. Electric motor provides power in the first region of operation whereas spring based assistive force is used in the second region and spring based resistive force is applied in the third region. This design concept provides an engineering solution of integrating three phases of post-stroke exercises in a single device. With this strategy, the energy source is only used in the first region to power the motor whereas the other two modes of exercise can work on the stored energy of springs. All these operations are controlled by a single motor and the maximum torque of the motor required is only 5 Nm. However, due to mechanical advantage, the exoskeleton can provide the joint torque up to 10 Nm. To remove the dependency on biosensor, the exoskeleton has been designed with a hardware-based mechanism that can provide assistive and resistive force. All exoskeleton components are integrated into a microcontroller-based circuit for measuring three joint parameters (angle, velocity and torque) and for controlling exercises. A user-friendly, multi-purpose graphical interface has been developed for participants to control the mode of exercise and it can be managed manually or in automatic mode. To validate the conceptual design, a prototype of the exoskeleton has been developed and it has been tested with healthy subjects. The generated assistive torque can be varied up to 0.037 Nm whereas resistive torque can be varied up to 0.057 Nm. The mass of the exoskeleton is approximately 1.8 kg. Two comparative studies have been performed to assess the measurement accuracy of the exoskeleton. In the first study, data collected from two healthy participants after using the exoskeleton and Kinect sensor by keeping Kinect sensor as reference. The mean measurement errors in joint angle are within 5.18 % for participant 1 and 1.66% for participant 2; the errors in torque measurement are within 8.48% and 7.93% respectively. In the next study, the repeatability of joint measurement by exoskeleton is analysed. The exoskeleton has been used by three healthy users in two rotation cycles. It shows a strong correction (correlation coefficient: 0.99) between two consecutive joint angle measurements and standard deviation is calculated to determine the error margin which comes under acceptable range (maximum: 8.897). The research embodied in this thesis presents a design framework of a portable exoskeleton model for providing three modes of exercises, which could provide a potential solution for all stages of post- stroke rehabilitation

MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

Error Recovery in Robot Systems

This dissertation addresses itself to the problem faced by a robot in recovering from failures during execution of a task. Failures occur partly because sensory information is inaccurate, partly because effectors do not always perform as expected, and partly because the domain in which the robot operates cannot be characterized exactly. Robot systems with automated planners have traditionally dealt with the problem of error recovery by merely replanning to achieve the desired goal, without attempting to characterize the failure in any way whatsoever. The central idea in this thesis is that planning recovery from failures has its own special techniques, distinct from those used in conventional planning systems. Two viewpoints, looking at the past for an explanation of the failure, and looking at the current situation to attempt a characterization of the failure state, provide powerful heuristics for error recovery. This thesis suggests that these heuristics can be formalized as failure reason analysis and multiple outcome analysis, and that knowledge relevant for such analysis can be provided through a failure reason model and a multiple outcome model associated with each action. The failure reason model about why actions provides a means for representing fail, like bumping into an object to be grasped because of servoing errors or because of inaccurate information about the location of the object. The model also provides knowledge required for distinguishing between the different reasons for failure. Finally, it includes recommendations of corrective actions to be taken if failure is attributed to a specific reason. This model in used in failure reason analysis in building a failure tree representing possible explanations of the failure. The explanations represented in the tree are then used in planning recovery. The multiple outcome model provides a way of representing the possible outcomes of an action, like bumping onto the object or bumping onto the ground in the immediate vicinity of the object, ignoring the fact that these outcomes could be the result of several different reasons. Knowledge required to distinguish between different outcomes is provided as part of the model. In cases where the immediately available information is inadequate to identify the outcome of an action, the multiple outcome model provides a basis for executing actions to serve as information gathering steps. The novel feature here is that information gathering is directed by specific expectations about the state of the world. A computer implementation of a program called MEND has provided a medium for exploring the above idea. MEND has been designed to automate recovery from failures in simple manipulation tasks to be performed by the JPL robot, but the techniques used in MEND have greater generality. A first implementation of MEND established the basis of this investigation. A second version, which has been designed to correct some limitations of the first version, has not yet been fully implemented and integrated with the JPL robot system. The techniques of planning recovery from failures through failure reason analysis and multiple outcome analysis are contributions to the subject of robotics. More importantly, however, the problem of error recovery is recognized to be a member of a larger class of problems involving knowledge representation and common sense reasoning, both of which are core topics in the study of artificial intelligence. The solution presented in this thesis makes some new contributions to these core topics.</p

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

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