发表机构
University of South Florida; United States Naval Academy（南佛罗里达大学; 美国海军学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于仿生内模型的在线估计器，通过优化方法从部分观测和自身运动重建未观测状态，并在机器人硬件上实时验证。
AI 中文摘要
用于追踪、跟踪和集体运动的仿生反馈控制通常以交互智能体之间的相对构型来表达。然而，在实际中，机载传感器可能无法直接提供反馈控制所需的全部量，因此需要对未观测到的量进行估计。本文开发了一种基于仿生内模型的估计器，用于从部分感官观测和已知的自身运动中重建这些量。状态重建被构建为一个优化问题，该问题将相对运动学视为约束，并最小化内模型输出与机载传感器测量之间的不一致性。使用庞特里亚金极大值原理推导必要的最优性条件。采用前向-后向算法提供数值解，并采用移动时域公式进行在线实现。该估计器在数值上针对经典状态估计器进行了评估。通过两种追踪策略，在机器人硬件上演示了所提出框架的实时实现。
英文摘要
Bioinspired feedback controls for pursuit, tracking, and collective motion are often expressed in terms of the relative configuration between interacting agents. In practice, however, onboard sensors may not directly provide all quantities required for feedback control, necessitating estimation of unobserved quantities. This paper develops a bioinspired internal model-based estimator for reconstructing those quantities from partial sensory observations and known self-motion. State reconstruction is posed as an optimization problem that treats the relative kinematics as constraints and minimizes the disagreement between the internal model outputs and measurements from onboard sensors. Pontryagin's Maximum Principle is used to derive the necessary optimality conditions. A forward-backward algorithm is used to provide a numerical solution and a moving horizon formulation is employed for online implementation. The estimator is evaluated numerically against classical state estimators. Real-time implementation of the proposed framework on robotic hardware is demonstrated through two pursuit strategies.
