TY - GEN
T1 - Dynamic Collision Avoidance for Slave Instruments in Robotic Cardiac Surgery
AU - Zang, Xizhe
AU - Wang, Peng
AU - Wang, Xu
AU - Chu, Hui
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Minimally invasive surgical robots have improved the safety and efficiency of cardiac surgery. With smaller and more flexible instruments and endoscopes, robotic systems reduce incision size and number. However, the limited operative space introduces a risk of collisions between instruments. Due to difficulties in accurate instrument localization via kinematics or sensors, this paper proposes a vision-based method for dynamic collision avoidance. Instrument positions are obtained through image segmentation, and depth maps are generated using a stereo-matching algorithm combined with a pre-trained depth estimation model, achieving an average relative error of around 7%. A KD-tree with a hierarchical strategy enables fast distance estimation. Based on instrument distance and master-side motion speed, a collision risk model is built to predict potential collisions and trigger avoidance strategies. The method is validated in a simulated environment.
AB - Minimally invasive surgical robots have improved the safety and efficiency of cardiac surgery. With smaller and more flexible instruments and endoscopes, robotic systems reduce incision size and number. However, the limited operative space introduces a risk of collisions between instruments. Due to difficulties in accurate instrument localization via kinematics or sensors, this paper proposes a vision-based method for dynamic collision avoidance. Instrument positions are obtained through image segmentation, and depth maps are generated using a stereo-matching algorithm combined with a pre-trained depth estimation model, achieving an average relative error of around 7%. A KD-tree with a hierarchical strategy enables fast distance estimation. Based on instrument distance and master-side motion speed, a collision risk model is built to predict potential collisions and trigger avoidance strategies. The method is validated in a simulated environment.
KW - Depth estimation
KW - Dynamic Collision Avoidance
KW - Robotic Cardiac Surgery
KW - Surgical Instrument Segmentation
UR - https://www.scopus.com/pages/publications/105020891851
U2 - 10.1007/978-981-95-2098-5_23
DO - 10.1007/978-981-95-2098-5_23
M3 - 会议稿件
AN - SCOPUS:105020891851
SN - 9789819520978
T3 - Lecture Notes in Computer Science
SP - 266
EP - 274
BT - Intelligent Robotics and Applications - 18th International Conference, ICIRA 2025, Proceedings
A2 - Matsuno, Takayuki
A2 - Liu, Honghai
A2 - Liu, Lianqing
A2 - Yin, Zhouping
A2 - Zhu, Xiangyang
A2 - Ren, Weihong
A2 - Wang, Zhiyong
A2 - Sheng, Yixuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Intelligent Robotics and Applications, ICIRA 2025
Y2 - 6 August 2025 through 9 August 2025
ER -