Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
Abstract
Generating stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This letter presents an alternative methodology, where we propose the addition of musculoskeletal models directly into the gait generation process to intuitively shape the resulting behavior. In particular, we construct a multi-domain hybrid system model that combines the system dynamics with muscle models to represent natural multicontact walking. Provably stable walking gaits can then be generated for this model via the hybrid zero dynamics (HZD) method. We experimentally apply our integrated framework towards achieving multicontact locomotion on a dual-actuated transfemoral prosthesis, AMPRO3, for two subjects. The results demonstrate that enforcing muscle model constraints produces gaits that yield natural locomotion (as analyzed via comparison to motion capture data and electromyography). Moreover, gaits generated with our framework were strongly preferred by the non-disabled prosthetic users as compared to gaits generated with the nominal HZD method, even with the use of systematic tuning methods. We conclude that the novel approach of combining robotic walking methods (specifically HZD) with muscle models successfully generates anthropomorphic robotic-assisted locomotion.
Additional Information
© 2022 IEEE. Manuscript received September 9, 2021; accepted January 24, 2022. Date of publication February 8, 2022; date of current version February 24, 2022. This letter was recommended for publication by Associate Editor Andrea Del Prete and Editor Abderrahmane Kheddar upon evaluation of the reviewers' comments. This work was supported in part by NSF GRF under Grant DGE-1745301, in part by Wandercraft, and in part by the ZEITLIN Fund, and conducted under IRB Number 21-0693.Attached Files
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Additional details
- Eprint ID
- 113404
- Resolver ID
- CaltechAUTHORS:20220210-721740000
- NSF Graduate Research Fellowship
- DGE-1745301
- Wandercraft
- Zeitlin Family Discovery Fund
- Created
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2022-02-11Created from EPrint's datestamp field
- Updated
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2022-03-10Created from EPrint's last_modified field