HIDDEN MARKOV MODEL ANALYSIS OF FORCE TORQUE INFORMATION IN TELEMANIPULATION
HIDDEN MARKOV MODEL ANALYSIS OF FORCE TORQUE INFORMATION IN TELEMANIPULATION
复制标题
DOI：
10.1177/027836499101000508
复制
发表时间：
1991-10-01
影响因子：
9.2
通讯作者：
LEE, P
中科院分区：
文献类型：
--
作者：
HANNAFORD, B;LEE, P
建立了机器人远程操作过程中传感器信息的预测与分析模型。该模型使用隐马尔可夫模型（马尔可夫网的随机函数；HMM）来描述任务结构、操作员或智能控制器的目标结构以及与环境相互作用产生的力和扭矩等传感器信号。马尔可夫过程部分编码任务序列/子目标结构，与每个子目标状态相关联的观测密度编码与执行该子目标相关的期望传感器信号。描述了基于任务的工程知识构建模型参数的方法。采用Viterbi算法对实验遥操作过程中测得的力信号进行基于模型的分析，实现了数据的分段。Baum-Welch算法用于从给定的实验中识别最可能的HMM。HMM实现了一个结构化的、基于知识的模型，具有明确的不确定性和成熟的、最优的识别算法。
A new model is developed for prediction and analysis of sensor information recorded during robotic performance of tasks by telemanipulation. The model uses the Hidden Markov Model (stochastic functions of Markov nets; HMM) to describe the task structure, the operator or intelligent controller's goal structure, and the sensor signals such as forces and torques arising from interaction with the environment. The Markov process portion encodes the task sequence/subgoal structure, and the observation densities associated with each subgoal state encode the expected sensor signals associated with carrying out that subgoal. Methodology is described for construction of the model parameters based on engineering knowledge of the task. The Viterbi algorithm is used for model based analysis of force signals measured during experimental teleoperation and achieves excellent segmentation of the data into subgoal phases. The Baum-Welch algorithm is used to identify the most likely HMM from a given experiment. The HMM achieves a structured, knowledge-based model with explicit uncertainties and mature, optimal identification algorithms.