Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: robotics
Description
Physical human-robot interaction (pHRI) has immense potential in fields like industry, military, rehabilitation, and robotic-surgery. However, as the field continues to grow in prominence, there are technical challenges that must be addressed, including safety/stability, adaptability, efficiency, user experience, and versatility. Enhancing pHRI is paramount to overcome these challenges and benefit numerous areas. This dissertation consists of different studies that focus on improving physical human-robot interaction through the development and implementation of various control methods. The first study investigates the lower bounds of robotic damping that humans can stably interact with in different arm postures. The results indicate that the human arm is less capable of adjusting to the unstable environments when it is close to the body and laterally displaced for the anterior-posterior (AP) and the medial-lateral (ML) directions, respectively. The second study proposes a multi-degree-of-freedom variable damping controller that balances stability and agility and reduces user effort in pHRI. The controller effectively reduces user effort while increasing agility without compromising stability. The third study presents a variable stiffness control method to provide intuitive and smooth force guidance during pHRI. This controller significantly reduces robotic force guidance and user effort while maintaining speed and accuracy of movement. Based on the findings from these studies, a biomechanics-based user-adaptive variable impedance control is proposed, which can be applied in a diverse set of applications to enhance the overall performance of coupled human-robot systems. This controller accounts for impedance properties of the human limbs and adaptively changes robotic damping, stiffness, and equilibrium trajectory based on online estimation of user's intent of motion and intent of movement direction while minimizing energy of the coupled human-robot system. Bayesian optimization was used to evaluate an unknown objective function and optimize noisy performance. The presented adaptive control strategy could reduce energy expenditure and achieve performance improvement in several metrics of stability, agility, user effort, smoothness, and user preference. All studies were validated and tested through several human experiments. Overall, the dissertation contributes to the field of pHRI by providing insights into the dynamics of human-robot interactions and proposing novel control strategies to enhance their performance.
ContributorsZahedi, Fatemeh (Author) / Lee, Hyunglae Prof. (Thesis advisor) / Berman, Spring Prof. (Committee member) / Marvi, Hamid Prof. (Committee member) / Yong, Sze Zheng Prof. (Committee member) / Zhang, Yu Prof. (Committee member) / Arizona State University (Publisher)
Created2023