{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T19:10:14Z","timestamp":1758741014202,"version":"3.44.0"},"reference-count":26,"publisher":"MIT Press","issue":"2","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Insect-inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios, as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated a robot along routes of up to 60 m using a single-layer neural network trained with an Infomax learning rule. Given the small size of the network that effectively encodes the route, here we investigate the limits of this method, challenging it to navigate longer routes, investigating the relationship between performance, view acquisition rate and dimension, network size, and robustness to noise. Our goal is both to determine the parameters at which this method operates effectively and to explore the profile with which it fails, both to inform theories of insect navigation and to improve robotic deployments. We show that effective memorization of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. We also show that the ideal view acquisition rate must be increased with route length for consistent performance. We further demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size\u2014an important consideration for applicability to small, lower-power robots\u2014and investigate the profile of memory failure, demonstrating increased confusion across the route as it extends in length. In this extension to previous work, we also investigate the form taken by the network weights as training extends and the areas of the image on which visual familiarity\u2013based navigation most relies. Additionally, we investigate the robustness of familiarity-based navigation to view variation caused by noise.<\/jats:p>","DOI":"10.1162\/artl_a_00459","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:30:28Z","timestamp":1730460628000},"page":"211-227","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Investigating the Limits of Familiarity-Based Navigation"],"prefix":"10.1162","volume":"31","author":[{"given":"Amany Azevedo","family":"Amin","sequence":"first","affiliation":[{"name":"University of Sussex, Department of Informatics. aa2645@sussex.ac.uk"}]},{"given":"Efstathios","family":"Kagioulis","sequence":"additional","affiliation":[{"name":"University of Sussex, Department of Informatics"}]},{"given":"Norbert","family":"Domcsek","sequence":"additional","affiliation":[{"name":"University of Sussex, Department of 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