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| Open-Source Leg | |
|---|---|
| Developer | Elliott J. Rouse |
| Release | October 2020 |
| Stable release | 2.5
/ July 2024 |
| Written in | Python, C |
| Operating system | Linux, ROS 2 |
| License | GPLv3 (software) Apache 2.0 (hardware) |
| Website | https://opensourceleg.org |
| Repository | https://github.com/neurobionics/opensourceleg |
The Open-Source Leg (OSL) is an open-source robotic knee–ankle prosthesis used in research on powered lower-limb prosthetics, gait biomechanics, and wearable robotics. The platform provides openly licensed mechanical designs, electronics schematics, firmware, and software tools intended to support reproducible experimentation and cross-laboratory comparison.[1]
The OSL was developed to address the lack of standardized research platforms for powered prosthetic legs. Prior to its introduction, laboratories typically built custom robotic leg systems, a process requiring substantial engineering resources and limiting comparability across studies.[2][3][4][5][6][7] Reviews of prosthetic technologies have noted that differences in hardware design can influence controller performance, complicating interpretation of results across research groups.[8][9]
The OSL was first described scientifically in 2018 at the IEEE BioRob Conference, which presented the initial mechanical design and characterization of the system.[1] A subsequent publication in 2020 in Nature Biomedical Engineering provided a detailed description of the platform’s architecture and early evaluation.[10]
The system has since been used in research involving musculoskeletal modeling,[11] sensor-fusion methods for gait prediction,[12] and functional mobility training with powered prostheses.[13]
The OSL consists of modular powered knee and ankle joints that share a common mechanical structure. The system uses high‑torque exterior‑rotor motors originally developed for aerial robotics, enabling lower transmission ratios than those used in many earlier prosthetic research platforms.[7] Lower transmission ratios improve backdrivability and reduce passive impedance, supporting more natural interaction between the user and device.
Both joints can be configured as rigid actuators or as series‑elastic actuators with selectable stiffness values, allowing investigation of compliant actuation and energy‑storage strategies.[14][15] Integrated sensing includes magnetic encoders, six‑axis load cells, and inertial measurement units.
Mechanical designs are maintained in a cloud‑based CAD environment, enabling version control and collaborative development.
The OSL software ecosystem includes embedded firmware, mid‑level joint controllers, and a high‑level Python SDK. Firmware manages motor control, sensor sampling, filtering, and safety monitoring. Mid‑level controllers support torque, position, and impedance control, drawing on established impedance‑control principles.[16]
The Python SDK provides tools for real‑time control, data logging, visualization, and integration with ROS 2. The software is distributed through GitHub and PyPI, with automated testing and continuous integration.[17]
The platform supports both Dephy and T‑Motor actuator variants, enabling researchers to select between cost and ease of integration.
The OSL has been used in studies involving:
Reviews of open-source medical devices have cited the OSL as an example of collaborative, transparent hardware development in rehabilitation robotics.[20][21]
The OSL ecosystem includes CAD models, electronics schematics, firmware, software libraries, documentation, and a public discussion forum. Research groups internationally use the platform for prosthetics research, gait analysis, and wearable robotics.
Open-source medical device research has emphasized the importance of community-driven development, transparent design files, and reproducibility, themes reflected in the OSL project.[20][21]
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LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice.
Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject.
See the advice page on large language models for more information.