ZIHANG LYU

Mr. ZIHANG LYU

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
21 September 2023 in Machines
Verified
Journal Article
Open access
PDF file
ZLZIHANG LYU
ZIHANG LYU
KKKen'ichi Koyanagi
Ken'ichi Koyanagi
KNKatsuki Nagahara
Katsuki Nagahara
HMHiroyuki Masuta
Hiroyuki Masuta
Fengyu LiFengyu Li
Fengyu Li
TOToru Oshima
Toru Oshima
The teleoperation feature of a leader–follower robot is used to operate the robot in a working environment that is dangerous to the operator. This paper proposes a method for estimating the intended operation of the operator of the leader–follower robot and generating behaviors for the follower robot. By generating partially autonomous robot behavior, our proposed method decreases the burden on the operator and realizes a safe controller that is robust against misoperation or disturbances. Owing to the impact of misoperation on estimation and the potential for unintended movement of the follower robot, our proposed estimation method relies on historical values of intended operation. The proposed method was verified through simulation using real operation inputs to the leader system based on practical scenarios, including misoperation.

29 September 2026 in Robotics
Verified
Journal Article
Open access
PDF file
ZLZIHANG LYU
ZIHANG LYU
KKKen'ichi Koyanagi
Ken'ichi Koyanagi
HMHiroyuki Masuta
Hiroyuki Masuta
AHMED ALMASSRIAHMED ALMASSRI
AHMED ALMASSRI
Fengyu LiFengyu Li
Fengyu Li
KSKei Sawai
Kei Sawai
Leader–follower robots employ teleoperation, enabling flexible and safe operation. A robotic surgical system, as a typical leader–follower system, enables remote manipulation in minimally invasive surgery. However, inappropriate operator inputs may be directly transmitted to the patient side, potentially compromising safety. In this study, we propose a framework for estimating the operator’s operating strategy and suppressing motions caused by inappropriate operator inputs. The framework represents operating strategies on a strategy map defined by the degrees of precision, non-precision, and risk. A simplified fuzzy inference is used to estimate the degrees of precision and non-precision based on manipulation force and hand velocity, while the degree of risk is evaluated using gaze detection combined with the force and pressure applied by the instrument. Using processed kinematic and video data from the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), we validated the framework across gesture scenarios, including simulated cases of inappropriate operation. The results suggest that the proposed framework demonstrates the theoretical feasibility of effectively mitigating potentially unsafe motions within the simulated environment while preserving essential operational flexibility.