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Abstract

This article describes a new statistical, model-based approach to building a contact-state observer for time-invariant con straints for a point in three dimensions. Three-dimensional constraint estimation and selection, and the application of these procedures to a planar, two-dimensional maze is described in detail. The observer uses measurements of the contact force and position, and prior information about the task encoded in a network, to determine the current location of the robot in the task-configuration space. Each node represents what the measurements will look like in a small region of configuration space by storing a predictive, statistical, measurement model. Construction of the task network requires a model of both the grasped part and the environment. The model makes the system robust to alignment errors, however, gross errors can occur if the topology of the modeled configuration space differs from the true topology. Arcs in the task network represent possible transitions between models. Beam search is used to match the measurement history against possible paths through the model network to estimate the most likely path for the robot. The re sulting approach provides a new decision process that can be used as an observer for event-driven manipulation program ming.

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A Model-Based Approach to Cartesian Manipulation Contact Sensing

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