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- Creators: Zhang, Wenlong
Description
Soft robots provide an additional measure of safety and compliance over traditionalrigid robots. Generally, control and modelling experiments take place using a
motion capture system for measuring robot configuration. While accurate, motion
capture systems are expensive and require re-calibration whenever the cameras are
adjusted. While advances in soft sensors contribute to a potential solution to sensing
outside of a lab environment, most of these sensing methods require the sensors to
be embedded into the soft robot arm. In this work, a more practical sensing method
is proposed using off-the-shelf sensors and a Robust Extended Kalman Filter based
sensor fusion method. Inertial measurement unit sensors and wire draw sensors are
used to accurately estimate the state of the robot. An explanation for the need for
sensor fusion is included in this work. The sensor fusion state estimate is compared to
a motion capture measurement along with the raw inertial measurement unit reading
to verify the accuracy of the results. The potential for this sensing system is further
validated through Linear Quadratic Gaussian control of the soft robot. The Robust
Extended Kalman Filter based sensor fusion shows an error of less than one degree
when compared to the motion capture system.
ContributorsStewart, Kyle James (Author) / Zhang, Wenlong (Thesis advisor) / Yong, Sze Zheng (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023