School of Engineering, RMIT University, Melbourne, VIC, 3083, Australia.
School of Engineering, RMIT University, Melbourne, VIC, 3083, Australia.
J Mech Behav Biomed Mater. 2024 Sep;157:106611. doi: 10.1016/j.jmbbm.2024.106611. Epub 2024 Jun 3.
Dynamic soft tissue characterisation is an important element in robotic minimally invasive surgery. This paper presents a novel method by combining neural network with recursive least square (RLS) estimation for dynamic soft tissue characterisation based on the nonlinear Hunt-Crossley (HC) model. It develops a radial basis function neural network (RBFNN) to compensate for the error caused by natural logarithmic factorisation (NLF) of the HC model for dynamic RLS estimation of soft tissue properties. The RBFNN weights are estimated according to the maximum likelihood principle to evaluate the probability distribution of the neural network modelling residual. Further, by using the linearisation error modelled by RBFNN to compensate for the linearised HC model, an RBFNN-based RLS algorithm is developed for dynamic soft tissue characterisation. Simulation and experimental results demonstrate that the proposed method can effectively model the natural logarithmic linearisation error, leading to improved accuracy for RLS estimation of the HC model parameters.
动态软组织特性是机器人微创手术中的一个重要元素。本文提出了一种新的方法，将神经网络与递归最小二乘（RLS）估计相结合，基于非线性亨特-克罗斯利（HC）模型对动态软组织特性进行特征描述。该方法开发了一个径向基函数神经网络（RBFNN），以补偿 HC 模型的自然对数分解（NLF）引起的误差，用于软组织结构的动态 RLS 估计。根据最大似然原理，对 RBFNN 的权值进行了估计，以评估神经网络建模残差的概率分布。此外，通过使用 RBFNN 建模的线性化误差来补偿线性化 HC 模型，提出了一种基于 RBFNN 的 RLS 算法，用于动态软组织特性描述。仿真和实验结果表明，该方法可以有效地对自然对数线性化误差进行建模，从而提高 HC 模型参数的 RLS 估计的准确性。
