NRI: INT: Collaborative Research: An Open-Source Framework for Continuous Torque Control of Intuitive Robotic Prosthetic Legs
NRI: INT: Collaborative Research: An Open-Source Framework for Continuous Torque Control of Intuitive Robotic Prosthetic Legs
批准号:
2024446
负责人:
Eric Rombokas
金额:
$56.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2024-09-30
中文摘要
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英文摘要
This project will establish an open source set of software control algorithms that will allow an open source robotic prosthetic leg to facilitate rhythmic and non-rhythmic interactions between the human user and the environment. This project builds upon the Open-Source Leg, which is a robust, inexpensive, robotic leg platform that can be easily manufactured, assembled, and programmed. The project's overarching goal is to enable customizable behaviors that are continuously cued by the movement of the user’s body. The project promotes the progress of science by creating open source control hardware and software for compliant actuators that extend the capabilities of the Open-Source Leg. The advantages of compliant torque control, combined with intuitive, expressive control from the user, represents a significant improvement over currently-available prosthetic legs. The project will advance the national health by developing and testing high-level control software that will allow users of the Open Source Leg to seamlessly navigate around obstacles and perform dynamic activities. The improved mobility provided by these technologies will improve the quality of life and functional capabilities of many people living with mobility impairment. Open source hardware and software lower barriers to access for robotic technologies, which makes these robots great candidates not only as assistive co-robots in healthcare and other applications but also as educational tools for undergraduate and graduate students.Emerging powered prostheses such as the NSF-funded Open-Source Leg have motors that can restore normative biomechanics to above-knee amputees, but these devices are limited by their control strategies to a small set of pre-defined, steady-state activities. Each activity is typically divided into a discrete progression of gait periods called phases, resulting in a large set of distinct controllers that struggle to continuously coordinate prosthetic limb motion with the user. Discrete control paradigms have not been able to facilitate transient behaviors like transitions between activities or non-rhythmic motions like stepping backwards or stepping over obstacles. Recently, a new control paradigm has emerged that continuously synchronizes or coordinates prosthetic limb motion to the user based on inertial measurements from the user’s body (e.g., the residual limb). However, prior implementations have been limited to lab-specific prosthetic leg designs with stiff actuators that rigidly enforce the kinematic mappings from user motion to prosthetic joint position rather than complying to varying environmental interactions. The recently developed Open-Source Leg presents a unique opportunity to integrate this state-of-the-art control paradigm in a universally accessible testbed with series elastic actuators that soften interactions between the user, prosthesis, and environment. The overall goals of this project are to 1) understand how to achieve closed-loop torque and impedance control in the series elastic actuator of the open-source leg despite unmodeled dynamics from its low-cost design, and 2) understand how to integrate high-fidelity joint impedance control with two novel continuous controllers that promise to allow users to flexibly and seamlessly navigate obstacles and perform dynamic activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(7)
专著(0)
科研奖励(0)
会议论文
Optimizing Representations of Multiple Simultaneous Attributes for Gait Generation Using Deep Learning
使用深度学习优化步态生成的多个同时属性的表示
DOI:
10.1109/tnsre.2023.3273599
发表时间:
2023
期刊:
IEEE Transactions on Neural Systems and Rehabilitation Engineering
影响因子:
4.9
作者:
[Sharma, Abhishek, Rombokas, Eric]
通讯作者:
Rombokas, Eric
DOI:
10.1109/tnsre.2022.3156884
发表时间:
2022-01-01
期刊:
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
影响因子:
4.9
作者:
[Sharma, Abhishek, Rombokas, Eric]
通讯作者:
Rombokas, Eric
国内基金
海外基金
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