Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC
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- Nag, Aneek
- Department of Electrical Engineering, Kyushu University
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- Huang, Shuo
- Department of Electrical Engineering, Kyushu University
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- Themelis, Andreas
- Department of Electrical Engineering, Kyushu University
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- Yamamoto, Kaoru
- Department of Electrical Engineering, Kyushu University
Description
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.
Journal
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- 2022 IEEE Conference on Control Technology and Applications (CCTA)
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2022 IEEE Conference on Control Technology and Applications (CCTA) 1135-1140, 2022
Institute of Electrical and Electronics Engineers: IEEE
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Keywords
Details 詳細情報について
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- CRID
- 1050298742740632704
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- ISSN
- 27680770
- 27680762
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- HANDLE
- 2324/6625739
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- Text Lang
- en
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- Article Type
- conference paper
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- Data Source
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- IRDB
- Crossref
- KAKEN
- OpenAIRE
