Hidden Markov Model analysis of force/torque information in telemanipulation
- Univ. of Washington, Seattle (United States)
- California Inst. of Tech., Pasadena (United States)
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-base model with explicit uncertainties and mature, optimal identification algorithms.
- OSTI ID:
- 5067678
- Journal Information:
- International Journal of Robotics Research; (United States), Vol. 10:5; ISSN 0278-3649
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
MANIPULATORS
COMPUTERIZED CONTROL SYSTEMS
ALGORITHMS
DATA PROCESSING
MATHEMATICAL MODELS
TORQUE
CONTROL SYSTEMS
EQUIPMENT
LABORATORY EQUIPMENT
MATERIALS HANDLING EQUIPMENT
MATHEMATICAL LOGIC
ON-LINE CONTROL SYSTEMS
ON-LINE SYSTEMS
PROCESSING
REMOTE HANDLING EQUIPMENT
990200* - Mathematics & Computers