Sajadi Seyed MohammadReza, Karbasi Seyed Mojtaba, Brun Henrik, Tørresen Jim, Elle Ole Jacob, Mathiassen Kim
The Research Group of Robotics and Intelligent Systems (ROBIN), Department of Informatics, University of Oslo, Oslo, Norway.
Digital Signal Processing Group, Department of Informatics, University of Oslo, Oslo, Norway.
Front Robot AI. 2022 Jun 15;9:896267. doi: 10.3389/frobt.2022.896267. eCollection 2022.
This paper presents the design, control, and experimental evaluation of a novel fully automated robotic-assisted system for the positioning and insertion of a commercial full core biopsy instrument under guidance by ultrasound imaging. The robotic system consisted of a novel 4 Degree of freedom (DOF) add-on robot for the positioning and insertion of the biopsy instrument that is attached to a UR5-based teleoperation system with 6 DOF. The robotic system incorporates the advantages of both freehand and probe-guided biopsy techniques. The proposed robotic system can be used as a slave robot in a teleoperation configuration or as an autonomous or semi-autonomous robot in the future. While the UR5 manipulator was controlled using a teleoperation scheme with force controller, a reinforcement learning based controller using the Deep Deterministic Policy Gradient (DDPG) algorithm was developed for the add-on robotic system. The dexterous workspace analysis of the add-on robotic system demonstrated that the system has a suitable workspace within the US image. Two sets of comprehensive experiments including four experiments were performed to evaluate the robotic system's performance in terms of the biopsy instrument positioning, and the insertion of the needle inside the ultrasound plane. The experimental results showed the ability of the robotic system for in-plane needle insertion. The overall mean error of all four experiments in the tracking of the needle angle was 0.446°, and the resolution of the needle insertion was 0.002 mm.
本文介绍了一种新型全自动机器人辅助系统的设计、控制和实验评估,该系统用于在超声成像引导下定位和插入商用全芯活检器械。该机器人系统由一个新型的4自由度附加机器人组成,用于活检器械的定位和插入,该附加机器人连接到一个基于UR5的具有6自由度的远程操作系统。该机器人系统融合了徒手活检技术和探头引导活检技术的优点。所提出的机器人系统可在远程操作配置中用作从属机器人,或在未来用作自主或半自主机器人。在使用带有力控制器的远程操作方案控制UR5机械手的同时,为附加机器人系统开发了一种基于深度确定性策略梯度(DDPG)算法的强化学习控制器。附加机器人系统的灵巧工作空间分析表明,该系统在超声图像内具有合适的工作空间。进行了两组包括四个实验的综合实验,以评估机器人系统在活检器械定位以及在超声平面内插入针方面的性能。实验结果表明了机器人系统进行平面内针插入的能力。在跟踪针角度的所有四个实验中,总体平均误差为0.446°,针插入的分辨率为0.002毫米。