{"id":3298,"date":"2026-09-22T23:57:45","date_gmt":"2026-09-22T13:57:45","guid":{"rendered":"https:\/\/braininspirednavigation.com\/?p=3298"},"modified":"2026-09-22T23:57:45","modified_gmt":"2026-09-22T13:57:45","slug":"how-to-implement-efficient-robot-navigation-inspired-by-honeybee-learning-flights","status":"publish","type":"post","link":"https:\/\/braininspirednavigation.com\/?p=3298","title":{"rendered":"How to implement efficient robot navigation inspired by honeybee learning flights?"},"content":{"rendered":"<p class=\"c-bibliographic-information__citation\" style=\"text-align: justify;\">Dequan Ou, Jesse J. Hagenaars, Maciej R. Jankowski, Michiel V. M. Firlefyn, Christophe De Wagter, Florian T. Muijres, Jacqueline Degen &amp; Guido C. H. E. de Croon. \u00a0<a href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3\"><strong>Efficient robot navigation inspired by honeybee learning flights<\/strong><\/a>. <i>Nature<\/i> <b>653<\/b>, 1039\u20131046 (2026). https:\/\/doi.org\/10.1038\/s41586-026-10461-3<\/p>\n<p style=\"text-align: justify;\">Abstract<\/p>\n<div id=\"Abs1-content\" class=\"c-article-section__content\">\n<p style=\"text-align: justify;\">&#8220;<strong><span style=\"color: #ff0000;\">Navigation is a crucial capability for both animals and robots<\/span><\/strong>. Although tiny flying insects can robustly navigate over long distances<sup><a id=\"ref-link-section-d13737758e489\" title=\"Beekman, M. &amp; Ratnieks, F. L. W. Long-range foraging by the honey-bee, Apis mellifera L. Funct. Ecol. 14, 490\u2013496 (2000).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR1\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 1\">1<\/a><\/sup>, state-of-the-art robot navigation methods are computationally expensive and therefore restricted to large robots<sup><a id=\"ref-link-section-d13737758e493\" title=\"Herrera-Granda, E. P., Torres-Cantero, J. C., Rosales, A. &amp; Peluffo-Ord\u00f3\u00f1ez, D. H. A comparison of monocular visual SLAM and visual odometry methods applied to 3D reconstruction. Appl. Sci. 13, 8837 (2023).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR2\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2\">2<\/a>,<a id=\"ref-link-section-d13737758e496\" title=\"Sharafutdinov, D. et al. Comparison of modern open-source visual SLAM approaches. J. Intell. Robot. Syst. 107, 43 (2023).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR3\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 3\">3<\/a><\/sup>. Here <strong><span style=\"color: #ff0000;\">we propose \u2018Bee-Nav\u2019, a highly efficient navigation strategy inspired by the visual learning flights of honeybees<\/span><\/strong><sup><a id=\"ref-link-section-d13737758e500\" title=\"Degen, J. et al. Exploratory behaviour of honeybees during orientation flights. Anim. Behav. 102, 45\u201357 (2015).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR4\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\">4<\/a>,<a id=\"ref-link-section-d13737758e500_1\" title=\"Capaldi, E. A. &amp; Dyer, F. C. The role of orientation flights on homing performance in honeybees. J. Exp. Biol. 202, 1655\u20131666 (1999).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR5\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\">5<\/a>,<a id=\"ref-link-section-d13737758e503\" title=\"Capaldi, E. A. et al. Ontogeny of orientation flight in the honeybee revealed by harmonic radar. Nature 403, 537\u2013540 (2000).\" href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3#ref-CR6\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 6\">6<\/a><\/sup>. In equivalent robotic learning flights, a tiny neural network is trained to map omnidirectional images to a home vector based on path integration. After learning, the robot can fly far away from home, come straight back using path integration and cancel integration drift using the visual homing network. Simulations showed that, for realistic path integration accuracies, the neural network requires training on only approximately 0.25\u201310.00% of the total flight area. In real-world indoor and outdoor experiments, a small drone successfully returned to within 0.5\u2009m of home for 100% of 30\u2013110-m flights and 70% of 200\u2013600-m flights in windy conditions, using 3.4-kB and 42-kB neural networks, respectively. <strong><span style=\"color: #ff0000;\">The proposed navigation strategy will be vital for resource-constrained robots that perform tasks while travelling from and to a home location<\/span><\/strong>. Furthermore, <strong><span style=\"color: #ff0000;\">it provides new perspectives on the neuroethology of insect navigation, from how visual learning shapes homing trajectories to the nature of cognitive maps<\/span><\/strong>.&#8221;<\/p>\n<p class=\"c-bibliographic-information__citation\" style=\"text-align: justify;\">Dequan Ou, Jesse J. Hagenaars, Maciej R. Jankowski, Michiel V. M. Firlefyn, Christophe De Wagter, Florian T. Muijres, Jacqueline Degen &amp; Guido C. H. E. de Croon. \u00a0<a href=\"https:\/\/www.nature.com\/articles\/s41586-026-10461-3\"><strong>Efficient robot navigation inspired by honeybee learning flights<\/strong><\/a>. <i>Nature<\/i> <b>653<\/b>, 1039\u20131046 (2026). https:\/\/doi.org\/10.1038\/s41586-026-10461-3<\/p>\n<p style=\"text-align: justify;\">\u00a0<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Dequan Ou, Jesse J. Hagenaars, Maciej R. Jankowski, Michiel V. M. Firlefyn, Christophe De Wagter, Florian T. Muijres, Jacqueline Degen &amp; Guido C. H. E. de Croon. \u00a0Efficient robot navigation inspired by honeybee learning flights. Nature 653, 1039\u20131046 (2026). https:\/\/doi.org\/10.1038\/s41586-026-10461-3 Abstract &#8220;Navigation is a crucial capability for both animals and robots. Although tiny flying insects [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[114,420],"tags":[1517,1508,85,627,1518],"_links":{"self":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3298"}],"collection":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3298"}],"version-history":[{"count":1,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3298\/revisions"}],"predecessor-version":[{"id":3299,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3298\/revisions\/3299"}],"wp:attachment":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3298"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3298"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3298"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}