Application of Grazing-Inspired Guidance Laws to Autonomous Information Gathering

Abstract

Domestic grazing animals follow simple, scalable rules to assign themselves trajectories to cover a pasture. We explain how to adapt these rules for an information gathering system based on a realistic robot motion model and Kalman filter based evidence grid that accounts for both bandwidth and sensor limitations. Our results show that this algorithm can meet or exceed the performance of state of the art field robotics systems, particularly when scalability and robustness to failure are required.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
ADA619036

Entities

People

  • Donald Sofge
  • J. K. Hedrick
  • Shih-yuan Liu
  • Thomas Apker

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Animal Behavior
  • Animals
  • Area Coverage
  • Bandwidth
  • Cells
  • Data Fusion
  • Detectors
  • Guidance
  • Information Surveillance
  • Kalman Filters
  • Mechanical Engineering
  • Military Research
  • Robots
  • Scalability
  • Simulations
  • Trajectories

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control