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Yanwei Qu

dblp:383/4601 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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 · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control
0.812024
A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot · ICRA 2024
Robotics › Motion planning and robot control › robot control › sensor-based control
vision-force control
0.812024
A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot · ICRA 2024
Medical and health informatics
surgical robotics
0.812024
A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot · ICRA 2024
Human-robot interaction › physical human-robot interaction
tactile interaction
0.212024
A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot · ICRA 2024

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

virtual wrench · 2.3remote center of motion · 2.3impedance control · 2.3
YearPublicationVenuePosition
2024 A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot
abstract
Laparoscope-holding robots significantly enhance the stability and precision of visualization in minimally invasive surgeries. Most existing robots of this kind depend on visual servo systems and struggle with efficient, rapid adjustments in the field-of-view (FOV), especially when identifying organs and needles outside the FOV. This paper presents a laparoscope-holding robot system capable of employing both vision-driven and force-driven mechanisms for continuous and large-scale FOV adjustments, respectively. The system features an integrated tactile handle, enabling the reception of human-robot interaction forces during surgical navigation. We propose a hybrid control method that leverages both force and vision inputs for laparoscopic FOV adjustments. This approach integrates a virtual wrench, generated from visual information, and an interaction wrench, obtained from the tactile handle, into the robot's dynamic model, which complies with remote center of motion constraints. The interaction wrench's gain is adjusted with the gripping force on the integrated tactile handle, ensuring that unintended movements caused by accidental contacts are prevented, thus safeguarding operational safety. The proposed method eliminates the need to switch control modes, enabling simultaneous visual tracking and tactile interaction guidance. Experimental results demonstrate that the proposed method not only allows for FOV adjustments with surgical instrument guiding but also adapts well to large-scale FOV adjustment tasks.
Xiaojian Li 0003, Hangjie Mo, Xilin Xiao, Yanwei Qu
ICRA7