机器地图及其概念模型

The Machine Map and its Conceptual Model

无人平台对复杂环境的自主认知能力是制约其广泛应用的关键问题,已成为当前地图学、人工智能、机器人等领域的研究热点。本文基于地图学的视角,交叉融合人工智能、机器人和认知科学的相关研究思路与成果,借鉴心象地图的作用功能,提出适用于无人平台认知环境的新型地图——机器地图,并对其特征进行了分析。类比心象地图的构建与运用机制,结合人工认知系统的设计决策点和核心认知能力,给出了机器地图概念模型的设计原则,提出“两环三图”的概念模型。该模型说明了机器地图与无人平台的内在联系,并勾勒出机器地图的功能部件与运行逻辑,为开展机器地图研究提供顶层框架和理论指导;类比心象地图的结构组成,提出了包含感知地图、工作地图和长时地图的机器地图逻辑构成,并分析了3类地图的内容与转化关系。在结论部分指出了机器地图的双向促进研究路线。机器地图的研究,能够提升无人自主平台对于复杂环境的认知和学习能力,也为智能时代地图学的发展探索新路径,具有十分重要的意义。

The autonomous cognition ability of unmanned platforms in complex environments is a key issue that restricts their real-world applications and has become a research hotspot in cartography,artificial intelligence,and robotics.Although the research on environment modeling and learning for unmanned platforms has achieved substantial progress,these platforms still face problems maintaining robustness,adaptability,and continuous learning when leaving well-trained environments for real-world environments.Motivated by cartographic research,this paper reviews the research work from artificial intelligence,robotics,and cognitive science and proposes a novel environment cognition model of unmanned platforms,the machine map.We first rationalize the similarity between the machine map and the mental map with a brief review of the mental map for human spatial cognition and then summarizes the machine map's characteristics.Having reviewed the research findings on the cognitive mechanism of mental maps,we propose the conceptual model of the machine map that features an architecture of"two cycles and three composition maps."The architecture follows design principles drawn from the research on the core cognitive capabilities of artificial cognitive systems.As for the two cycles,the outer cycle demonstrates the machine map's function in an autonomous unmanned platform,while the inner cycle illustrates the key components and the operation logic among them.Motivated by the structure theory of a mental map,the machine map is modeled as a multi-store memory system that consists of a perception map,a working map,and a long-term map.The overall information processing procedure among these three composition maps is discussed to finalize the model design.The conceptual development of machine maps benefits from studying the mental map in cognitive research and the technical innovations in autonomous driving and robotics fields,such as High-Definition maps,SLAM,and BEV.The proposed conceptual model can serve as a top-level research framework and a route map for further research on machine maps.In the end,the paper suggests that the research of machine maps needs a two-way methodology.On the top level,the deductive reasoning of the conceptual model can promote the understanding of the connotation and architecture of machine maps.While on the bottom level,the continuous development of machine learning and artificial intelligence technology can mitigate the restrictions on the environmental cognitive ability of unmanned platforms,resulting in a continuous improvement of the technical framework of machine maps.The research on the machine map can improve the cognitive capabilities of autonomous unmanned platforms in complex environments and illuminate a new path for the development of cartography in the intelligent era.We hope this paper can raise interest in machine maps among the cartographic community and thus promote the development of this emerging field.