Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC

DOI DOI 機関リポジトリ (HANDLE) PDF 被引用文献1件 参考文献23件 オープンアクセス
  • Nag, Aneek
    九州大学大学院システム情報科学府電気電子工学専攻
  • Huang, Shuo
    九州大学大学院システム情報科学府電気電子工学専攻
  • Themelis, Andreas
    九州大学大学院システム情報科学研究院電気システム工学部門
  • 山本, 薫
    九州大学大学院システム情報科学研究院電気システム工学部門

説明

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.

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