记忆神经网络在机器人导航领域的应用与研究进展

Research progress and application of memory neural network in robot navigation

记忆神经网络非常适合解决时间序列决策问题,将其用于机器人导航领域是非常有前景的新兴研究领域。本文主要讨论记忆神经网络在机器人导航领域的研究进展。给出几种基本记忆神经网络结合导航任务的工作机理,总结了不同模型的优缺点;对记忆神经网络在导航领域的研究进展进行简要综述;进一步介绍导航验证环境的发展;最后梳理了记忆神经网络在导航问题所面临的复杂性挑战,并预测了记忆神经网络在导航领域未来的发展方向。

Memory networks are a relatively new class of models designed to alleviate the problem of learning long-term dependencies in sequential data,by providing an explicit memory representation for each token in the sequence,and they can be used for learning navigation policies in an unstructured terrain,which is a complex task.Memory neural networks are highly suitable for solving time series decision-making problems,and their application in robot navigation is a very promising and emerging research field.The research progress of memory neural networks in the field of robot navigation is primarily discussed in this paper.First,the working mechanism of several basic memory neural networks used for robot navigationis introduced,and the advantages and disadvantages of different models are summarized.Then,the research progress of memory neural network in navigation field is briefly reviewed,and the development of navigation verification environment is discussed.Finally,the complex challenges faced by memory neural networks in navigation are summarized,and the future development of memory neural networks in navigation field is predicted.