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- All Subjects: Mechanical Engineering
- All Subjects: soft robotics
Description
This paper presents the design of a pneumatic actuator for a soft ankle-foot orthosis, called the Multi-material Actuator for Variable Stiffness (MAVS). This pneumatic actuator consists of an inflatable soft fabric actuator fixed between two layers of rigid retainer pieces. The MAVS is designed to be integrated with a soft robotic ankle-foot orthosis (SR-AFO) exosuit to aid in supporting the human ankle in the inversion/eversion directions. This design aims to assist individuals affected with chronic ankle instability (CAI) or other impairments to the ankle joint. The MAVS design is made from compliant fabric materials, layered and constrained by thin rigid retainers to prevent volume increase during actuation. The design was optimized to provide the greatest stiffness and least deflection for a beam positioned as a cantilever with a point load. The design of the MAVS took into account passive stiffness of the actuator when combining rigid and compliant materials so that stiffness is maximized when inflated and minimal when passive. An analytic model of the MAVS was created to evaluate the effects in stiffness observed by varying the ratio in length between the rigid pieces and the soft actuator. The results from the analytic model were compared to experimentally obtained results of the MAVS. The MAVS with the greatest stiffness was observed when the gap between the rigid retainers was smallest and the rigid retainer length was smallest. The MAVS design with the highest stiffness at 100 kPa was determined, which required 26.71 ± 0.06 N to deflect the actuator 20 mm, and a resulting stiffness of 1,335.5 N/m and 9.1% margin of error from the model predictions.
ContributorsHertzell, Tiffany (Author) / Lee, Hyunglae (Thesis director) / Sugar, Thomas (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
For the basis of this project, a particular interest is taken in soft robotic arms for the assistance of daily living tasks. A detailed overview and function of the soft robotic modules comprised within the soft robotic arm will be the main focus. In this thesis, design and fabrication methods of fabric reinforced textile actuators (FRTAs) have their design expanded. Original design changes to the actuators that improve their performance are detailed in this report. This report also includes an explanation of how the FRTA’s are made, explaining step by step how to make each sub-assembly and explain its function. Comparisons between the presented module and the function of the soft poly limb from previous works are also expanded. Various forms of testing, such as force testing, range of motion testing, and stiffness testing are conducted on the soft robotic module to provide insights into its performance and characteristics. Lastly, present plans for various forms of future work and integration of the soft robotic module into a full soft robotic arm assembly are discussed.
ContributorsSeidel, Sam (Author) / Zhang, Wenlong (Thesis director) / Sugar, Thomas (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
For my thesis I worked in ASU’s Bio-Inspired Mechatronics lab on a project lead by PhD student Pham H. Nguyen (Berm) to develop an assistive soft robotic supernumerary limb. I contributed to the design and evaluation of two prototypes: the silicon based Soft Poly Limb (SPL) and one bladder-based fabric arm, the fabric Soft Poly Limb (fSPL). For both arms I was responsible for the design of 3D printed components (molds, end caps, etc.) as well as the evaluation of the completed prototypes by comparing the actual performance of the arms to the finite element predictions. I contributed to the writing of two published papers describing the design and evaluation of the two arms. After the completion of the fSPL I attempted to create a quasi-static model of the actuators driving the fSPL.
ContributorsSparks, Curtis Mitchell (Author) / Sugar, Thomas (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
ContributorsQIAO, ZHI (Author) / Zhang, Wenlong (Thesis advisor) / Marvi, Hamidreza (Committee member) / Lee, Hyunglae (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
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