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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Network Architecture for Cognitive Navigation in Dynamic Environments
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Neural Network Architecture for Cognitive Navigation in Dynamic Environments

机译：动态环境下认知导航的神经网络架构

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摘要

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.
机译：在具有不断变化的目标和障碍物的时变环境中导航需要认知能力，即使最简单的动物也能广泛展现这种能力。然而，对于人工制剂而言，这是一个长期存在的挑战性问题。应对这一问题的认知自主机器人必须解决两个基本任务：1）根据可能发生的事情以及如何应对这一问题来了解环境； 2）学习成功的经验，并以自动的潜意识方式进一步使用它们。最近引入的紧凑内部表示（CIR）概念为这两项任务奠定了基础。 CIR是一种特定的认知地图，它将不断演变的情况压缩为包含导航所需信息的静态结构。它属于全局方法的类别，即，当存在目标时，它会找到目标的轨迹，但在找不到解决方案时，它会检测情况。在这里，我们通过移动目标扩展情境的概念。然后，以CIR为核心，我们提出了一种由有意识和潜意识的途径组成的闭环神经网络体系结构，以进行有效的决策。如果默认的潜意识路径无法将代理引导至目标，则意识路径可为新型情况提供解决方案。通过使用粗纱机器人的实验和数值模拟，我们证明了所提出的体系结构为机器人提供了认知能力，并在现实的时空环境中实现了可靠而灵活的导航。我们证明了潜意识途径对感觉信息的不确定性具有鲁棒性。因此，如果一种新颖的情况与先前的经历相似但不相同（由于例如嘈杂的感知），则潜意识途径能够提供有效的解决方案。

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