Kou Bin, Ren Dongcheng, Guo Shijie
Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Guanghua Lingang Engineering Application and Technology R & D (Shanghai) Co., Ltd., Shanghai 201306, China.
Bioengineering (Basel). 2022 Jan 29;9(2):58. doi: 10.3390/bioengineering9020058.
To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot.
为提高常见智能算法在识别医疗机器人几何误差参数时的准确性,本文提出了一种改进的甲虫触角搜索算法(RWSAVSBAS)。我们首先建立了医疗机器人运动学误差模型,然后将狼群算法的随机游走行为添加到甲虫触角搜索算法的搜索过程中,以增强其局部搜索能力。在此基础上,利用模拟退火算法提高甲虫触角搜索算法的全局收敛能力。通过仿真将所提算法与蛙跳算法以及变步长甲虫触角搜索算法的末端定位精度进行比较。结果表明,所提算法具有更高的收敛精度,能够显著提高医疗机器人末端定位的准确性。