Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC
Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC
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DOI：
10.1109/ccta49430.2022.9966067
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发表时间：
2022-02
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影响因子：
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通讯作者：
Aneek Nag;Shuo Huang;Andreas Themelis;Kaoru Yamamoto
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文献类型：
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作者：
Aneek Nag;Shuo Huang;Andreas Themelis;Kaoru Yamamoto
本文提出了一种基于模型预测控制（MPC）的方法，利用每个agent计算的未来轨迹预测来解决具有避障能力的leader-follower框架中的群体控制问题。我们采用传统的雷诺兹群集规则（内聚、分离和对齐）作为基础，并调整模型以适应导航（而不是形成）的目的。特别地，我们引入了几个概念，例如从邻居收集的信息的可信度和重要性，以及引用之间的动态权衡。它们基于以下观察：近未来的预测更可靠，离领导者更近的代理是受过更多教育的信息的隐性载体，内聚或结盟的优势取决于代理与其邻居之间的距离。这些特点都纳入了MPC公式，并通过数值模拟讨论了它们的优点。
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.