Probabilistic swarm guidance using optimal transport

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Control Applications, CCA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479974092
StatePublished - Dec 9 2014
Event2014 IEEE Conference on Control Applications, CCA 2014 - Juan Les Antibes, France
Duration: Oct 8 2014Oct 10 2014

Publication series

Name2014 IEEE Conference on Control Applications, CCA 2014


Other2014 IEEE Conference on Control Applications, CCA 2014
CityJuan Les Antibes

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering


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