一种骨科机器人磨削作业规划的算法
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摘要
骨科机器人是医疗领域的新兴产业,主要用于微创、精确治疗,可提供精确的手术导航和规划,但目前市场上现存的产品普遍存在自动化程度低,损伤大,辐射高,效率低的问题。针对关节置换时对骨关节磨削实现全自动化的需求,提出一种基于骨关节手术部位分片的方法,并结合遗传算法对磨削路径进行了路径优化。通过MATLAB与VREP的联合仿真证明算法的可行性,并有效的提高骨科机器人的自动化程度。与传统半自动骨科机器人相比,方法能够有效提高手术效率,减少医生疲劳,确保病人得到精度更高的医疗服务。
Orthopedic robots are emerging industries in the medical field, mainly used for minimally invasive and precise treatment, which can provide precise surgical navigation and planning. However, the existing products in the current market generally have problems of low automation, large damage, high radiation and low efficiency. In this paper, aiming at the need of full automation of bone joint grinding during joint replacement, a method based on bone joint surgical site segmentation is proposed, and the path optimization of grinding path is carried out by combining genetic algorithm. The joint simulation of MATLAB and VREP proves the feasibility of the algorithm and effectively improves the degree of automation of orthopaedic robot. Compared with the traditional semi-automatic orthopedic robots, this method can effectively improve the operation efficiency, reduce doctor fatigue and ensure patients to get more accurate medical services.
Orthopedic robots are emerging industries in the medical field, mainly used for minimally invasive and precise treatment, which can provide precise surgical navigation and planning. However, the existing products in the current market generally have problems of low automation, large damage, high radiation and low efficiency. In this paper, aiming at the need of full automation of bone joint grinding during joint replacement, a method based on bone joint surgical site segmentation is proposed, and the path optimization of grinding path is carried out by combining genetic algorithm. The joint simulation of MATLAB and VREP proves the feasibility of the algorithm and effectively improves the degree of automation of orthopaedic robot. Compared with the traditional semi-automatic orthopedic robots, this method can effectively improve the operation efficiency, reduce doctor fatigue and ensure patients to get more accurate medical services.
引文
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[2] W A olf,et al.MBARS:mini bone-attached robotic system for joint arthroplasty[J].International Journal of Medical Robotics & Computer Assisted Surgery,2005,1(2):101.
[3] N Sugita,et al.Development of a computer-integrated minimally invasive surgical system for knee arthroplasty[C].The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics,20-22 Feb,2006:323-328.
[4] R H Taylor,D Stoianovici.Medical robotics in computer-integrated surgery[J].IEEE Transactions on Robotics & Automation,2003,19(5):765-781.
[5] A D Pearle,et al.Perioperative management of unicompartmental knee arthroplastyusing the MAKO robotic arm system ( MAKOplasty)[J].American Journal of Orthopedics,2009,38( 2):16-19.
[6] J H Lonner.Robotically Assisted Unicompartmental Knee Arthroplasty with a Handheld Image-Free Sculpting Tool[J].Orthopedic Clinics of North America,2016,47(1):29.
[7] B Davies,et al.Active-Constraint Robotics for Surgery[J].Proceedings of the IEEE,2006,94(9):1696-1704.
[8] 邾继贵,等.大型空间复杂曲面无干扰精密测量方法[J].光学学报,2010,30(12):3524-3529.
[9] 柯映林,等.反求工程CAD建模理论、方法和系统[M].北京:机械工业出版社,2005.
[10] 张静,等.基于广义典型相关分析融合和鲁棒概率协同表示的人脸指纹多模态识别[J].上海理工大学学报,2018,(2).
[11] 周圣熊.全膝关节置换手术中机器人切骨:系统构建与模拟实验[D].广东工业大学,2016.
[12] M J Kang,C G Han.Solving the rural postman problem using a genetic algorithm with a graph transformation[C].Symposium on Applied Computing.ACM,1998:356-360.
[13] 文修,梁怡.遗传算法的数学基础[J].西安交通大学学报,2000,34(12):6
[14] 周现甫.遗传算法的原理及应用[J].科技展望,2017,27(3).
[15] 周明,孙树栋.遗传算法原理及应用[M].国防工业出版社,1999.