Design of an Elastic Actuation System for a Gait-Assistive Active Orthosis for Incomplete Spinal Cord Injured Subjects

A spinal cord injury severely reduces the quality of life of affected people. Following the injury, limitations of the ability to move may occur due to the disruption of the motor and sensory functions of the nervous system depending on the severity of the lesion. An active stance-control knee-ankle-foot orthosis was developed and tested in earlier works to aid incomplete SCI subjects by increasing their mobility and independence. This thesis aims at the incorporation of elastic actuation into the active orthosis to utilise advantages of the compliant system regarding efficiency and human-robot interaction as well as the reproduction of the phyisological compliance of the human joints. Therefore, a model-based procedure is adapted to the design of an elastic actuation system for a gait-assisitve active orthosis. A determination of the optimal structure and parameters is undertaken via optimisation of models representing compliant actuators with increasing level of detail. The minimisation of the energy calculated from the positive amount of power or from the absolute power of the actuator generating one human-like gait cycle yields an optimal series stiffness, which is similar to the physiological stiffness of the human knee during the stance phase. Including efficiency factors for components, especially the consideration of the electric model of an electric motor yields additional information. A human-like gait cycle contains high torque and low velocities in the stance phase and lower torque combined with high velocities during the swing. Hence, the efficiency of an electric motor with a gear unit is only high in one of the phases. This yields a conceptual design of a series elastic actuator with locking of the actuator position during the stance phase. The locked position combined with the series compliance allows a reproduction of the characteristics of the human gait cycle during the stance phase. Unlocking the actuator position for the swing phase enables the selection of an optimal gear ratio to maximise the recuperable energy. To evaluate the developed concept, a laboratory specimen based on an electric motor, a harmonic drive gearbox, a torsional series spring and an electromagnetic brake is designed and appropriate components are selected. A control strategy, based on impedance control, is investigated and extended with a finite state machine to activate the locking mechanism. The control scheme and the laboratory specimen are implemented at a test bench, modelling the foot and shank as a pendulum articulated at the knee. An identification of parameters yields high and nonlinear friction as a problem of the system, which reduces the energy efficiency of the system and requires appropriate compensation. A comparison between direct and elastic actuation shows similar results for both systems at the test bench, showing that the increased complexity due to the second degree of freedom and the elastic behaviour of the actuator is treated properly. The final proof of concept requires the implementation at the active orthosis to emulate uncertainties and variations occurring during the human gait

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

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A comparison between a two-feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

Design and Control of Robotic Systems for Lower Limb Stroke Rehabilitation

Lower extremity stroke rehabilitation exhausts considerable health care resources, is labor intensive, and provides mostly qualitative metrics of patient recovery. To overcome these issues, robots can assist patients in physically manipulating their affected limb and measure the output motion. The robots that have been currently designed, however, provide assistance over a limited set of training motions, are not portable for in-home and in-clinic use, have high cost and may not provide sufficient safety or performance. This thesis proposes the idea of incorporating a mobile drive base into lower extremity rehabilitation robots to create a portable, inherently safe system that provides assistance over a wide range of training motions. A set of rehabilitative motion tasks were established and a six-degree-of-freedom (DOF) motion and force-sensing system was designed to meet high-power, large workspace, and affordability requirements. An admittance controller was implemented, and the feasibility of using this portable, low-cost system for movement assistance was shown through tests on a healthy individual. An improved version of the robot was then developed that added torque sensing and known joint elasticity for use in future clinical testing with a flexible-joint impedance controller

Simulating a Flexible Robotic System based on Musculoskeletal Modeling

Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

On the motion/stiffness decoupling property of articulated soft robots with application to model-free torque iterative learning control

This paper tackles the problem of controlling articulated soft robots (ASRs), i.e., robots with either fixed or variable elasticity lumped at the joints. Classic control schemes rely on high-authority feedback actions, which have the drawback of altering the desired robot softness. The problem of accurate control of ASRs, without altering their inherent stiffness, is particularly challenging because of their complex and hard-to-model nonlinear dynamics. Leveraging a learned anticipatory action, Iterative Learning Control (ILC) strategies do not suffer from these issues. Recently, ILC was adopted to perform position control of ASRs. However, the limitation of position-based ILC in controlling variable stiffness robots is that whenever the robot stiffness profile is changed, a different input action has to be learned. Our first contribution is to identify a wide class of ASRs, whose motion and stiffness adjusting dynamics can be proved to be decoupled. This class is described by two properties that we define: strong elastic coupling - relative to motors and links of the system, and their connections - and homogeneity - relative to the characteristics of the motors. Furthermore, we design a torque-based ILC scheme that, starting from a rough estimation of the system parameters, refines the torque needed for the joint positions tracking. The resulting control scheme requires minimum knowledge of the system. Experiments on variable stiffness robots prove that the method effectively generalizes the iterative procedure w.r.t. the desired stiffness profile and allows good tracking performance. Finally, potential restrictions of the method, e.g., caused by friction phenomena, are discussed