结合反馈与预测控制的仿人机器人仿生视觉追踪模型

E. Falotico, Lorenzo Vannucci, Nicola Di Lecce, P. Dario, C. Laschi
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引用次数: 2

摘要

人类能够通过自发的平滑的眼球运动来追踪移动的视觉目标。平滑追踪眼球运动的目的是最小化投射到视网膜上的目标速度(视网膜滑动)。由于视觉信息处理的延迟,基于负反馈的控制无法实现这一点。在本文中,我们提出了一个适合机器人实现的模型,能够将视觉反馈和平滑追踪的预测控制的主要特征集成在一起。该模型由用于反馈控制的逆动力学控制器、用于目标运动预测的神经预测器和能够适当地结合先前系统的加权和模块组成。我们的研究结果在仿人机器人的模拟眼睛模型上进行了测试,结果表明该模型可以使用预测进行零滞后视觉跟踪,使用基于反馈的控制进行“不可预测”的目标跟踪,并根据目标动态适当地将这两种方法相结合,以保证稳定的视觉追踪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bio-inspired model of visual pursuit combining feedback and predictive control for a humanoid robot
Humans are able to track a moving visual target by generating voluntary smooth pursuit eye movements. The purpose of smooth pursuit eye movements is to minimize the target velocity projected onto the retina (retinal slip). This is not achievable by a control based on a negative feedback due to the delays in the visual information processing. In this paper we propose a model, suitable for a robotic implementation, able to integrate the main characteristics of visual feedback and predictive control of the smooth pursuit. The model is composed of an inverse dynamics controller for the feedback control, a neural predictor for the anticipation of the target motion and an Weighted Sum module that is able to combine the previous systems in a proper way. Our results, tested on a simulated eye model of our humanoid robot, show that this model can use prediction for a zero-lag visual tracking, use a feedback based control for “unpredictable” target pursuit and combine these two approaches properly switching from one to the other, depending on the target dynamics, in order to guarantee a stable visual pursuit.
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