Unmanned platforms are rapidly being used in many different fields. However, improving their cognition and understanding of complex environments remains a challenging research problem. Machine maps are a new class of maps proposed to address this problem. Based on the conceptual model of the machine map, this paper adopts the research perspective of cognitive science and further proposes a theoretical model of the machine map’s construction, which is cognitive, plausible, and consistent with the cognitive structure. This paper first discusses the theoretical roots of machine maps in cognitive science in terms of the origin, formation, and development of machine maps. Second, this paper briefly reviews the research on the structure and generation of memory models, mental image maps, cognitive architectures, and environmental cognition issues of robotic systems. Furthermore, it discusses the cognitive structure of the machine map and its supporting role in the map construction model of machine maps. Third, this paper proposes the design principles of the machine map’ s construction model, which includes the organization of environment information using distributed representations, structural design of the machine map using a multistore memory system, and modeling of the generation of the machine map with a reference of brain cognitive activities. Furthermore, this paper presents the task objectives, content classification, detailed logical structure, and generation of the machine map’s construction. The perceptual map conducts preliminary processing of information acquired by sensors to obtain information about the features, location, geometry, and semantics of entities in the surrounding environment. The working map is functionally similar to the working memory in human brains, which contains visual information, spatial information, situational information, and specialized maps constructed to accomplish specific tasks. The long-time map uses perceptual map and working map as information sources, and the fragmented information in the perceptual map and working map is associated, managed, and processed more extensively to form an environment model with global reference. Finally, the machine map generation’s primary activities (e.g., understanding, attention, inference, learning, and action) and processes (e.g., implicit map generation and explicit map generation) are discussed based on the logical structure. Implicit map generation refers to the process in which the content and knowledge in the long-term map are continuously enriched and accumulated through the continuous evolution and support of the perceptual map and the working map during the operation of the unmanned platform. This process contains three activities: shallow understanding, deep understanding, and implicit learning. Explicit map generation refers to the process in which the working map forms a specialized map for a given task to meet the specific task requirements and supports the generation of spatial behavior under the support of itself, perceptual map, and long-term map. The process consists of six activities: superficial understanding, inference, attention, deep understanding, episodic learning, and action. The cognitive structure and map construction model, which is an interpretation of the machine map cognitive computing system, can serve as a basic framework for researchers interested in the machine map, enabling them to carry out collaborative research at a more abstract level, and provide references for the integration, evaluation, and application of related technologies and data. This paper also describes new requirements and goals for constructing digital twin or virtual geographic environments. © 2024 Science Press. All rights reserved.
