Suppr超能文献

蚂蚁的视觉路线导航:行为的精细细节如何促进成功的路线导航及趋同。

Ant visual route navigation: How the fine details of behaviour promote successful route performance and convergence.

作者信息

Amin Amany Azevedo, Philippides Andrew, Graham Paul

机构信息

Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom.

Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom.

出版信息

PLoS Comput Biol. 2025 Sep 10;21(9):e1012798. doi: 10.1371/journal.pcbi.1012798. eCollection 2025 Sep.

Abstract

Individually foraging ants use egocentric views as a dominant navigation strategy for learning and retracing routes. Evidence suggests that route retracing can be achieved by algorithms which use views as 'visual compasses', where individuals choose the heading that leads to the most familiar visual scene when compared to route memories. However, such a mechanism does not naturally lead to route approach, and alternative strategies are required to enable convergence when off-route and for correcting on-route divergence. In this work we investigate how behaviour incorporated into visual compass like route learning and recapitulation strategies might enable convergence to a learned route and its destination. Without alterations to the basic form of the initial learning route, the most successful recapitulation method comes from a 'cast and surge' approach, a mechanism seen across arthropods for olfactory navigation. In this strategy casts form a 'zig-zagged' or oscillatory search in space for familiar views, and surges exploit visual familiarity gradients. We also find that performance improves if the learned route consists of an oscillatory motor mechanism with learning gated to occur when the agent approaches the central axis of the oscillation. Furthermore, such oscillations combined with the cast and surge method additively enhance performance, showing that it benefits to incorporate oscillatory behaviour in both learning and recapitulation. As destination reaching is the primary goal of navigation, we show that a suitably sized goal-orientated learning walk might suffice, but that the scale of this is dependent on the degree of divergence, and thus depends on route length and the route learning and recapitulation strategies used. Finally we show that view familiarity can modulate on-the-spot scans performed by an agent, providing a better reflection of ant behaviour. Overall, our results show that the visual compass can provide a basis for robust visual navigation, so long as it is considered holistically with the details of basic motor and sensory-motor patterns of ants undertaking route learning and recapitulation.

摘要

单独觅食的蚂蚁将以自我为中心的视角作为学习和回溯路线的主要导航策略。有证据表明,路线回溯可以通过将视角用作“视觉指南针”的算法来实现,即个体在与路线记忆相比时,选择通向最熟悉视觉场景的方向。然而,这种机制并不能自然地导致路线接近,需要替代策略来在偏离路线时实现收敛并纠正路线上的偏差。在这项工作中,我们研究了纳入视觉指南针的行为,如路线学习和重现策略,如何能够实现收敛到已学习的路线及其目的地。在不改变初始学习路线基本形式的情况下,最成功的重现方法来自一种“投射与激增”方法,这是一种在节肢动物嗅觉导航中可见的机制。在这种策略中,投射在空间中形成“之字形”或振荡搜索以寻找熟悉的视角,而激增则利用视觉熟悉度梯度。我们还发现,如果学习路线由振荡运动机制组成,并且在智能体接近振荡中心轴时进行学习门控,性能会有所提高。此外,这种振荡与投射和激增方法相加会提高性能,表明在学习和重现中纳入振荡行为是有益的。由于到达目的地是导航的主要目标,我们表明适当大小的目标导向学习行走可能就足够了,但这一规模取决于偏差程度,因此取决于路线长度以及所使用的路线学习和重现策略。最后,我们表明视角熟悉度可以调节智能体进行的现场扫描,更好地反映蚂蚁的行为。总体而言,我们的结果表明,视觉指南针可以为稳健的视觉导航提供基础,只要在考虑蚂蚁进行路线学习和重现的基本运动和感觉运动模式细节时进行全面考虑。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1412/12445746/28eaa8cd26fd/pcbi.1012798.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验