# Visuospatial Working Memory for Autonomous UAVs: A Bio-Inspired Computational Model
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs), also known as drones, are becoming more and more popular in the research community because they have been widely studied in order to know their potential usage in different areas such as entertainment [1], marketing [2], healthcare [3], agriculture [4], and security [5]. The Artificial Intelligence (AI) research community has made a great effort to develop \"intelligent\" UAVs capable of doing tasks in an autonomous or semi-autonomous way [6]. Creating an \"intelligent\" UAV capable of navigating and interacting autonomously in the real world involves facing formidable challenges because they need to be able to process multiple tasks simultaneously, such as constantly sensing the environment; identifying and classifying both static and dynamic obstacles and targets; generating an internal representation of the real world, where the spatial relationship information between the environment's objects must be constantly updated; reasoning and making right decisions to react appropriately when unexpected events appear. New interdisciplinary fields such as cognitive computing [7] and cognitive infocommunication [8] aim to create various bio-inspired engineering applications such as brain-computer interfaces [9] and computational systems capable of mimicking theintelligence of living beings (e.g., insects, rodents, primates, and humans) [10; 11; 12; 13; 14]. The computing research community focused on developing bio-inspired systems considers that nature offers a rich source of inspiration for developing \"intelligent\" systems. Undoubtedly, one of the most challenging bio-inspired systems is the system that tries to mimic human intelligence. This paper presents a bio-inspired computational model of Visuospatial Working Memory (VSWM) to endow autonomous UAVs with this cognitive function. The VSWM is a fundamental cognitive component of human beings for exploring the visual environment. The VSWM is a memory buffer that allows observers to retain visuospatial information for a short period of time when visual stimuli are no longer in view as a consequence of object occlusion and saccadic eye movements [15]. The VSWM is formed by two types of Working Memory (WM): Visual Working Memory (VWM) and Spatial Working Memory (SWM) [16]. Whereas the VWM is a short-term memory involved in the active representation of the visual appearance of relevant objects [15; 16], the SWM is a short-term memory involved in the active representation of the locations of relevant objects [16; 17]. Therefore, the VSWM integrates sensory information coming from both the VWM and the SWM to generate visuospatial maps, also known as visuospatial mental imagery [16; 18; 19]. These maps offer a coherent and continuous representation of visual and spatial relationships among objects present in the external world, which are then further processed for the generation of specific actions such as spatial shifting, visual search, and target selection. The bio-inspired VSWM computational model proposed in this paper is not a unique model. Other bio-inspired models have been proposed and reported in the literature. In [10], a bio-inspired model known as RatSLAM was described. This model has been inspired by current knowledge of how rodents' brains work. RatSLAM has been implemented on ground robots and tested in both indoor and outdoor environments. In [20], a computational model for visual processing and object recognition was proposed. This computational model has been based on the current knowledge of the human brain areas related to the visual object recognition cognitive function. However, this computational model was focused only on one part of the human visual system, which is involved in the visual object recognition process, without considering the human brain areas involved in the spatial information. Additionally, this computational model is limited to work with a single visual object formed by lines, which is presented in an environment without noise and distractors.