Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho

Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype.publishedVersio

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

Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.Comment: 16 pages journal articl

Hierarchical Compliance Control of a Soft Ankle Rehabilitation Robot Actuated by Pneumatic Muscles

Traditional compliance control of a rehabilitation robot is implemented in task space by using impedance or admittance control algorithms. The soft robot actuated by pneumatic muscle actuators (PMAs) is becoming prominent for patients as it enables the compliance being adjusted in each active link, which, however, has not been reported in the literature. This paper proposes a new compliance control method of a soft ankle rehabilitation robot that is driven by four PMAs configured in parallel to enable three degrees of freedom movement of the ankle joint. A new hierarchical compliance control structure, including a low-level compliance adjustment controller in joint space and a high-level admittance controller in task space, is designed. An adaptive compliance control paradigm is further developed by taking into account patient’s active contribution and movement ability during a previous period of time, in order to provide robot assistance only when it is necessarily required. Experiments on healthy and impaired human subjects were conducted to verify the adaptive hierarchical compliance control scheme. The results show that the robot hierarchical compliance can be online adjusted according to the participant’s assessment. The robot reduces its assistance output when participants contribute more and vice versa, thus providing a potentially feasible solution to the patient-in-loop cooperative training strateg

Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art

Robot-assisted rehabilitation has become a new mainstream trend for the treatment of stroke patients with movement disability. Pneumatic muscle (PM) is one of the most promising actuators for rehabilitation robots, due to its inherent compliance and safety features. In this paper, we conduct a systematic review on the soft rehabilitation robots driven by pneumatic muscles. This review discusses up to date mechanical structures and control strategies for PMs-actuated rehabilitation robots. A variety of state-of-the-art soft rehabilitation robots are classified and reviewed according to the actuation configurations. Special attentions are paid to control strategies under different mechanical designs, with advanced control approaches to overcome PM’s highly nonlinear and time-varying behaviors and to enhance the adaptability to different patients. Finally, we analyze and highlight the current research gaps and the future directions in this field, which is potential for providing a reliable guidance on the development of advanced soft rehabilitation robots

Design, Control, and Perception of Bionic Legs and Exoskeletons

Bionic systems---wearable robots designed to replace, augment, or interact with the human body---have the potential to meaningfully impact quality of life; in particular, lower-limb prostheses and exoskeletons can help people walk faster, better, and safer. From a technical standpoint, there is a high barrier-to-entry to conduct research with bionic systems, limiting the quantity of research done; additionally, the constraints introduced by bionic systems often prohibit accurate measurement of the robot's output dynamics, limiting the quality of research done. From a scientific standpoint, we have begun to understand how people regulate lower-limb joint impedance (stiffness and damping), but not how they sense and perceive changes in joint impedance. To address these issues, I first present an open-source bionic leg prosthesis; I describe the design and testing process, and demonstrate patients meeting clinical ambulation goals in a rehabilitation hospital. Second, I develop tools to characterize open-loop impedance control systems and show how to achieve accurate impedance control without a torque feedback signal; additionally, I evaluate the efficiency of multiple bionic systems. Finally, I investigate how well people can perceive changes in the damping properties of a robot, similar to an exoskeleton. With this dissertation, I provide technical and scientific advances aimed at accelerating the field of bionics, with the ultimate goal of enabling meaningful impact with bionic systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163108/1/afazocar_1.pd

Underactuated Rehabilitation Robotics for Hand Function

Normal hand function plays an important role in daily life. At present, the incidence of hand dysfunction caused by diseases such as cerebral palsy or stroke is increasing year by year. For the rehabilitation of hand dysfunction, in addition to surgical treatment, effective rehabilitation exercise is also particularly important. It is also a necessary link in the efficient and intelligent development of rehabilitation medicine to develop robots that can effectively help patients with rehabilitation hand functions.In this paper, based on the analysis of the design principles and objectives of the rehabilitation robot with hand function, the kinematics model of the rehabilitation robot with hand function is constructed,based on top-down principle in the design of the machine, the design of the machine hand function rehabilitation robots design optimization process framework, and based on the kinematics model and the virtual prototype technology, build its skeleton model, and carries on the kinematics simulation analysis, the design is verified the correctness of the hand function rehabilitation robot kinematics model