Probabilistic Swarm Guidance using Optimal Transport

Abstract

Probabilistic swarm guidance enables autonomous agents to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. In contrast with previous homogeneous or inhomogeneous Markov chain based approaches [1], this paper presents an optimal transport based approach which guarantees faster convergence, minimizes a given cost function, and reduces the number of transitions for achieving the desired formation. Each agent first estimates the current swarm distribution by communicating with neighboring agents and using a consensus algorithm and then solves the optimal transport problem, which is recast as a linear program, to determine its transition probabilities. We discuss methods for handling motion constraints and also demonstrate the superior performance of the proposed algorithm by numerically comparing it with existing Markov chain based strategies.

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Document Details

Document Type
Technical Report
Publication Date
Oct 10, 2014
Accession Number
AD1015567

Entities

People

  • Fred Y. Hadaegh
  • Saptarshi Bandyopadhyay
  • Soon-Jo Chung

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Agents
  • Boundaries
  • Collision Avoidance
  • Consensus Algorithms
  • Control Systems
  • Convex Programming
  • Electronic Mail
  • Guidance
  • Jet Propulsion
  • Linear Programming
  • Markov Chains
  • Model Predictive Control
  • Monte Carlo Method
  • Probability
  • Simulations
  • Trajectories

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
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  • Mathematical Modeling and Probability Theory.