Coordinating Robotic Networks through Belief Propogation

Abstract

For this project, we have primarily addressed problem of wireless coverage using a team of mobile robots with ad-hoc communications through the development of distributed probabilistic coordination models and algorithms. For coverage, a team of mobile robots, each with limited sensing and communications ranges, must provide a set of communication chains that cover as large a space as possible. Our goal was to develop robot coordination algorithms that perform communications chaining accurately and robustly in the face of various uncertainty factors, such as noise in sensing and wireless signal strength, with secondary consideration of constrained resources, such as power, computation, and communications bandwidth. We proposed and developed robot software to reach this goal based on the Markov Random Fields (MRFs) as a probabilistic model for distributed inference. The Multi-robot Markov Random Field model allowed us to unify existing algorithms for multi-robot coordination, such as the commonly used potential field, in a generative model and propose new coordination algorithms using belief propagation. Based on this model, we produced a plug-and-play software framework for distributed multi-robot coordination on top of the Robot Operating System (ROS). This framework [3] enabled empirical comparison of belief propagation coordination against baseline algorithms (such the potential field) in various environments, both simulated and with real robots. We additionally explored using this same MRF model for coordination problems over other forms of complex networks, where global solutions result from local coordination. Specifically in the area of manifold learning, we used a MRF-based model to develop BP-Isomap, a belief propagation version of Isomap nonlinearity dimension reduction. BP-Isomap [4] probabilistically extended manifold learning towards greater robustness to noise in neighborhood selection.

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Document Details

Document Type
Technical Report
Publication Date
Sep 12, 2012
Accession Number
ADA577130

Entities

People

  • Odest C. Jenkins

Organizations

  • Brown University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Ad Hoc Networks
  • Algorithms
  • Computer Vision
  • Department Of Defense
  • Dimensionality Reduction
  • Environment
  • Generative Models
  • Mesh Networks
  • Mobile Ad Hoc Networks
  • Models
  • Networks
  • Open Source Software
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Robots

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computer Networking
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers