Contact Sensing: A Sequential Decision Approach to Sensing Manipulation Contact Features.

Abstract

This paper describes a new statistical, model-based approach to building a contact state observer. The observer uses measurements of the contact force and position, and prior information about the task encoded in a graph, 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. This approach assumes that the measurements are statistically block independent conditioned on knowledge of the model, which is a fairly good model of the actual process. Arcs in the graph represent possible transitions between models. Beam Viterbi search is used to match measurement history against possible paths through the model graph in order to estimate the most likely path for the robot. The resulting approach provides a new decision process that can be use as an observer for event driven manipulation programming. The decision procedure is significantly more robust than simple threshold decisions because the measurement history is used to make decisions. The approach can be used to enhance the capabilities of autonomous assembly machines and in quality control applications. (KAR) P. 2

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1995
Accession Number
ADA297805

Entities

People

  • Brian S. Eberman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Change Detection
  • Computational Science
  • Control Systems
  • Data Science
  • Geometric Forms
  • Geometry
  • Information Science
  • Mathematical Filters
  • Motion Planning
  • Network Science
  • Random Variables
  • Signal Processing
  • Statistical Algorithms
  • Surveys
  • Three Dimensional
  • Two Dimensional

Readers

  • Computational Modeling and Simulation
  • Computer Programming and Software Development.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers