Single-port three-arm robotic tool designed for precise neurosurgery use
Design and Modeling of a Single-Port Three-Arm Robotic Tool for Minimally Invasive Neurosurgery
Robotics
Summary
Performing surgery inside the brain is tricky because surgeons have very little space to work through small openings. The authors designed a tiny robotic tool with three flexible arms that can all fit through one small tube. This tool can be guided precisely using a robotic arm and has many ways it can move, allowing it to reach hard spots inside the brain. The authors tested the design carefully and confirmed their mathematical models matched how the robot actually moved.
What this means in practice
- •For medical device engineers: Develop compact robotic tools that operate multiple arms for delicate procedures through a single small opening in neurosurgery.$Commercial implications: Enables the creation of multi-arm surgical robots for minimally invasive brain surgeries that can be marketed to hospitals and surgical teams.
- •For industrial robotic designers: Design compact multi-arm robotic tools capable of operating within confined spaces with precise control.
Authors
Nazia H. Dana, Harith S. Gallage, Ismail A. Auta, Dhanvi Yuvaraj, Ronghuai Qi
Abstract
Surgical robots require highly dexterous and compact robotic systems capable of operating effectively within confined anatomical spaces. However, due to limited access provided by a single incision, the miniaturization and maneuverability of these robots still need to be improved. In this paper, we propose the design and modeling of a single-port three-arm robotic tool containing one major cannula (7.14 mm outer diameter (OD)) and three steerable minor cannulas (1.93 mm OD). By integrating the proposed 12 degrees-of-freedom (DoFs) steerable robotic tool with a 7-DoF robotic arm, this robotic system can potentially achieve multi-arm manipulation capability. We present the design of the steerable robotic tool consisting of tendon-driven joints controlled by a compact actuation system, derive the kinematic model, and validate both the static and kinematic models through experiments. The performance is evaluated with the root mean square error (RMSE) and mean absolute error (MAE) computed between the experimental data and the kinematic model.