Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC

Aneek Nag, Shuo Huang, Andreas Themelis, Kaoru Yamamoto

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

Abstract

We propose a model predictive control (MPC) based approach to a flock control problem with obstacle avoidance capability in a leader-follower framework, utilizing the future trajectory prediction computed by each agent. We employ the traditional Reynolds' flocking rules (cohesion, separation, and alignment) as a basis, and tailor the model to fit a navigation (as opposed to formation) purpose. In particular, we introduce several concepts such as the credibility and the importance of the gathered information from neighbors, and dynamic trade-offs between references. They are based on the observations that near-future predictions are more reliable, agents closer to leaders are implicit carriers of more educated information, and the predominance of either cohesion or alignment is dictated by the distance between the agent and its neighbors. These features are incorporated in the MPC formulation, and their advantages are discussed through numerical simulations.

Original languageEnglish
Title of host publication2022 IEEE Conference on Control Technology and Applications, CCTA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1135-1140
Number of pages6
ISBN (Electronic)9781665473385
DOIs
Publication statusPublished - 2022
Event2022 IEEE Conference on Control Technology and Applications, CCTA 2022 - Trieste, Italy
Duration: Aug 23 2022Aug 25 2022

Publication series

Name2022 IEEE Conference on Control Technology and Applications, CCTA 2022

Conference

Conference2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Country/TerritoryItaly
CityTrieste
Period8/23/228/25/22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Automotive Engineering
  • Control and Systems Engineering
  • Control and Optimization

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