Performance Analysis of the Enhanced Bio-Inspired Planning Algorithm for Rapid Situation Awareness Response

Abstract

The virtual motion camouflage varying manifold based optimal planning algorithm, inspired by the observation in mating hoverflies, is investigated and the performance is enhanced significantly in terms of the convergence speed and collision avoidance capability. The dimension and time complexities of the innovative algorithm are analyzed. A new strategy based on local pursuit is compared with the virtual motion strategy in constructing the varying manifold. The algorithm is validated in an enhanced low cost robot testbed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 18, 2013
Accession Number
ADA588765

Entities

People

  • Yunjun Xu

Organizations

  • University of Central Florida

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Camouflage
  • Collision Avoidance
  • Collisions
  • Convergence
  • Government Procurement
  • Governments
  • Graphical User Interface
  • Nonlinear Programming
  • Observation
  • Situational Awareness
  • Space Situational Awareness
  • Spacecraft
  • Trajectories

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Autonomy