Problem
Research questions and friction points this paper is trying to address.
bioinspired feedback controls
partial sensory observations
state reconstruction
internal model-based estimator
online implementation
A bioinspired internal model-based online estimator for planar pursuit
🤖 AI Summary
本文提出一种基于生物启发的内部模型在线估计器，通过优化方法解决平面追踪中未观测量的估计问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
bioinspired internal model
state reconstruction
Pontryagin's Maximum Principle
moving horizon formulation
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