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Y. Shapiro

dblp:135/8266 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 77% Robot manipulation · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control
0.212013
A haptic surface scanning and machining parallel manipulator for registration-free bone resurfacing during arthroplasty · ICRA 2013
Robotics › Robot manipulation
haptic interface
0.012013
A haptic surface scanning and machining parallel manipulator for registration-free bone resurfacing during arthroplasty · ICRA 2013

Methods — techniques the papers use, named apart from their topics

registration-free machining · 0.2force-controlled scanning · 0.2curvature estimation · 0.2
YearPublicationVenuePosition
2013 A haptic surface scanning and machining parallel manipulator for registration-free bone resurfacing during arthroplasty
abstract
As orthopaedic implants become more sophisticated and complex, shaping the bone surface to match the implant becomes increasingly important. Accurate bone machining contributes to success of the implant and can potentially increase the implant's endurance and lifetime. Bone-mounted robots can eliminate intra-operative tracking errors during bone machining. However, registration errors still exist when aligning the pre-operative plan with the patient's anatomy because the plan is based on pre-operative images. Thus, a robotic system that is capable of executing both surface acquisition and bone machining in a single procedure and within the same coordinate system is preferable to the current state of the art. In this study we used a mini bone-attached robotic system (MBARS) with a haptic interface, to examine the accuracy of force-controlled surface acquisition of a femur model. The average distance error between acquired points and the surface model was less than 1 mm, which was further reduced by 5% with a compensation method which included curvature estimation of the reconstructed surface. An initial evaluation of in-situ bone machining based on these force-controlled scans yielded promising results.
Ifat Gertler, Y. Shapiro, Alon Wolf
ICRA2