Narayan Jyotindra, Singla Ekta, Soni Sanjeev, Singla Ashish
1 Department of Mechanical Engineering, Thapar Institute of Engineering & Technology, Patiala, India.
2 Mechanical Engineering Department, Indian Institute of Technology Ropar (IIT Ropar), Rupnagar, India.
Proc Inst Mech Eng H. 2018 Jul;232(7):726-732. doi: 10.1177/0954411918781418. Epub 2018 Jun 12.
Over the last few decades, medical-assisted robots have been considered by many researchers, within the research domain of robotics. In this article, a 5-degrees-of-freedom spatial medical manipulator is analyzed for path planning, based on inverse kinematic solutions. Analytical methods have generally employed for finding the inverse kinematic solutions in earlier studies. However, this method is only appreciable in case of closed-form solutions. The unusual joint configurations of considered manipulator result in more complexity to attain the closed-form solutions, analytically. To overcome with shortcomings of analytical method, a non-traditional approach named adaptive neuro-fuzzy inference system is proposed under the class of artificial intelligent techniques. This article presents this neuro-fuzzy approach for desired path generation by 5-degrees-of-freedom manipulator. The estimation of percentage error between actual path and adaptive neuro-fuzzy inference system-generated path is done with respect to x, y, and z directions, respectively. Furthermore, the error between actual and predicted values regarding joint parameters is calculated for a certain arm matrix. The prototype of 5-degrees-of-freedom medical-assisted manipulator is developed at CSIR-CSIO Laboratory Chandigarh, which is also termed as patient-side manipulator to be utilized in robot-assisted surgery. Through the simulation runs, in this work, it is found that the results from adaptive neuro-fuzzy inference system approach are quite satisfactory and acceptable.
在过去几十年里，许多研究人员在机器人技术研究领域中都考虑过医疗辅助机器人。在本文中，基于逆运动学解对一个五自由度空间医疗机械手进行路径规划分析。在早期研究中，通常采用解析方法来求逆运动学解。然而，这种方法仅在闭式解的情况下适用。所考虑的机械手的特殊关节配置使得通过解析方法获得闭式解变得更加复杂。为了克服解析方法的缺点，在人工智能技术类别下提出了一种名为自适应神经模糊推理系统的非传统方法。本文介绍了这种神经模糊方法用于五自由度机械手生成期望路径。分别针对x、y和z方向计算实际路径与自适应神经模糊推理系统生成路径之间的百分比误差估计。此外还针对特定的手臂矩阵计算了关节参数的实际值与预测值之间的误差。五自由度医疗辅助机械手的原型是在昌迪加尔的CSIR - CSIO实验室开发的，它也被称为患者侧机械手，用于机器人辅助手术。通过这项工作中的模拟运行发现，自适应神经模糊推理系统方法的结果相当令人满意且可以接受。
