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How animals navigate over large-scale environments remains a riddle. Specifically, it is debated whether animals have cognitive maps. The hallmark of map-based navigation is the ability to perform shortcuts, i.e., to move in direct but novel routes. When tracking an animal in the wild, it is extremely difficult to determine whether a movement is truly novel because the animal's past movement is unknown. We overcame this difficulty by continuously tracking wild fruit bat pups from their very first flight outdoors and over the first months of their lives. Bats performed truly original shortcuts, supporting the hypothesis that they can perform large-scale map-based navigation. We documented how young pups developed their visual-based map, exemplifying the importance of exploration and demonstrating interindividual differences.
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http://dx.doi.org/10.1126/science.aay3354 | DOI Listing |
Neuropsychologia
August 2025
School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6AL, UK. Electronic address:
Navigation means getting from here to there. Unfortunately, for biological navigation, there is no agreed definition of what we might mean by 'here' or 'there'. Computer vision ('Simultaneous Localisation and Mapping', SLAM) uses a 3D world-based coordinate frame but that is a poor model for biological spatial representation.
View Article and Find Full Text PDFComput Biol Med
August 2025
Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, 75 Francis St., Boston, 02115, MA, USA.
Introduction: Vision-Based Tracking (VBT) enhances Electromagnetic Tracking (EMT) by addressing Computer CT-to-body divergence. Current VBT methods use depth map-based point clouds to register the bronchoscope in Computer Tomography (CT) scans; however, similarities in airway geometry often lead to registration ambiguities. This paper introduces a Direct Pose Estimation (DPE) method for VBT that bypasses depth maps to localize the bronchoscope in CT scans, validated with human-derived lung phantoms.
View Article and Find Full Text PDFAdv Sci (Weinh)
August 2025
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Effective navigation of microswimmers relies on their ability to search for unknown target locations using limited information provided by local environmental cues. Biological microswimmers have evolved versatile strategies to achieve such mapless navigation. Yet, achieving autonomous navigation in artificial microswimmers, comparable to that of their biological counterparts, remains a significant challenge.
View Article and Find Full Text PDFBiol Psychol
July 2025
College of Education, National Tsing Hua University, Hsinchu City 30013, Taiwan; Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu City 30013, Taiwan; Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu City 30013, Taiwan;
Spatial perspective taking (SPT) is a core cognitive ability essential for real-world navigation, yet the examination of dynamic integration of allocentric and egocentric reference frames using map has received limited attention. To address this gap, the present study introduces a Map-Based Self-Localization Task paired with event-related potential (ERP) techniques to examine how angular disparities and rotation directions affect SPT. High school participants (n = 38) completed 320 Map-Based Self-Localization trials.
View Article and Find Full Text PDFSensors (Basel)
June 2025
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.
In recent research on path planning for cellular-connected Unmanned Aerial Vehicles (UAVs), leveraging navigation models based on complex networks and applying the A* algorithm has emerged as a promising alternative to more computationally intensive methods, such as deep reinforcement learning (DRL). These approaches offer performance that approaches that of DRL, while addressing key challenges like long training times and poor generalization. However, conventional A* algorithms fail to consider critical UAV flight characteristics and lack effective obstacle avoidance mechanisms.
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