Visuospatial Working Memory for Autonomous UAVs: A Bio-Inspired Computational Model
Source abstract
Abstract
Visuospatial working memory is a fundamental cognitive capability of human beings needed for exploring the visual environment. This cognitive function is responsible for creating visuospatial maps, which are useful for maintaining a coherent and continuous representation of visual and spatial relationships among objects present in the external world. A bio-inspired computational model of Visuospatial Working Memory (VSWM) is proposed in this paper to endow Autonomous Unmanned Aerial Vehicles (UAVs) with this cognitive function. The VSWM model was implemented on a low-cost commercial drone. A total of 30 test cases were designed and executed. These test cases were grouped into three scenarios: (i) environments with static and dynamic vehicles, (ii) environments with people, and (iii) environments with people and vehicles. The visuospatial ability of the VSWM model was measured in terms of the ability to classify and locate objects in the environment. The VSWM model was capable of maintaining a coherent and continuous representation of visual and spatial relationships among interest objects presented in the environment even when a visual stimulus is lost because of a total occlusion. The VSWM model proposed in this paper represents a step towards autonomous UAVs capable of forming visuospatial mental imagery in realistic environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Visual Attention and Saliency Detection · Memory and Neural Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, Universidad de Guadalajara, Ameca 46600, Mexico
Division of Graduate Studies and Research, Instituto Tecnológico de Ciudad Victoria, Ciudad Victoria 87010, Mexico
Department of Computer Science, Universidad Autónoma de Guadalajara, Zapopan 45129, Mexico
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