Ken Goldberg on Open Source Stewart Platform
Ken Goldberg and AutoLab have posted video and papers on an open-source Stewart platform design. UC Berkeley’s Laboratory for Automation Science and Engineering (AUTOLAB), directed by Prof. Goldberg, is a center for research in robotics and automation with 20 graduate and undergraduate students pursuing projects in Cloud Robotics, Deep Reinforcement Learning, Learning from Demonstrations, Computer Assisted Surgery, Automated Manufacturing, and New Media Artforms.
An “open-source” Stewart Platform Research Kit (SPRK) for simulating heartbeats and breathing for surgical robotics and a new algorithm for automated surgical cutting based on intermittent synchronization was presented on March 1, 2018 at the first International Symposium on Medical Robotics (ISMR) in Atlanta, GA.
Watch the video here.
ISMR also accepted two papers on the subject.
SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics.
Vatsal Patel, Sanjay Krishnan, Aimee Goncalves, Ken Goldberg.
Abstract:
To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a lowcost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm × 20cm×10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of ± 1.27 cm in translation along x, y, and z, and of ± 15◦ in roll, pitch, and yaw directions. The platform also has motion modes for sinusoidal motion, breathing-inspired motion, and multi-axis motion. Modular mounts facilitate pattern cutting and debridement experiments. The platform’s positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. Construction directions, CAD models, and control software for the platform are available at github.com/BerkeleyAutomation/sprk
Read the paper here.
Using Intermittent Synchronization to Compensate for Rhythmic Body Motion During Autonomous Surgical Cutting and Debridement.
Vatsal Patel, Sanjay Krishnan, Aimee Goncalves, Carolyn Chen, Walter Doug Boyd, Ken Goldberg.
Abstract:
Anatomical structures are rarely static during a surgical procedure due to breathing, heartbeats, and peristaltic movements. Inspired by observing an expert surgeon, we propose an intermittent synchronization with the extrema of the rhythmic motion (i.e., the lowest velocity windows). We performed 2 experiments: (1) pattern cutting and (2) debridement. In (1), we found that the intermittent synchronization approach, while 1.8× slower than tracking motion, is significantly more robust to noise and control latency, and it reduces the max cutting error by 2.6× except when motion is along 3 or more orthogonal axes. In (2), a baseline approach with no synchronization succeeds in 62% of debridement attempts while intermittent synchronization achieves an 80% success rate.
Read the paper here.
Read more about AutoLab at their website here.