Insect-inspired visually guided autonomous route navigation through natural environments

受昆虫启发的视觉引导自然环境自主路线导航

• 批准号: EP/I031758/1
• 负责人: Andrew Philippides
• 金额: $ 13.04万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI031758%2F1
• 关键词: Insect; inspired; visually; guided; autonomous

Our overall objective is to develop algorithms for long distance route-based visual navigation through complex natural environments. Despite recent advances in autonomous navigation, especially in map-based simultaneous localisation and mapping (SLAM), the problem of guiding a return to a goal location through unstructured, natural terrain is an open issue and active area of research. Despite their small brains and noisy low resolution sensors, insects navigate through such environments with a level of performance that outstrips state-of-the-art robot algorithms. It is therefore natural to take inspiration from insects. There has been a history of bio-inspired navigation models in robotics but there are known components of insect behaviour yet to be incorporated into engineering solutions. In contrast with most modern robotic methods, to navigate between two locations, insects, use procedural route knowledge and not mental maps. An important feature of route navigation is that the agent does not need to know where it is at every point (in the sense of localizing itself within a cognitive map), but rather what it should do. Insects provide further inspiration for navigation algorithms through their innate behavioural adaptations which simplify navigation through unstructured, cluttered environments.One objective is to develop navigation algorithms which capture the elegance and desirable properties of insect homing strategies - robustness (in the face of natural environmental variation), parsimony (of mechanism and visual encoding), speed of learning (insects must learn from their first excursion) and efficacy (the simple scale over which insects forage). Prior to this we will bring together current insights regarding insect behaviour with novel technologies which allow us to recreate visual input from the perspective of foraging insects. This will lead to new tools for biologists and increase our understanding of insect navigation. In order to achieve these goals our Work Packages will be:WP1 Development of tools for reconstructing large-scale natural environments. We will adapt an existing panoramic camera system to enable reconstruction of the visual input experienced by foraging bees. Similarly, we will adapt new computer vision methods to enable us to build world models of the cluttered habitats of antsWP2 Investigation of optimal visual encodings for navigation. Using the world model developed in WP1, we will investigate the stability and performance of different ways of encoding a visual sceneWP3 Autonomous route navigation algorithms. We will test a recently developed model of route navigation and augment it for robust performance in natural environmentsOur approach in this project is novel and timely. The panoramic camera system has just been developed at Sussex. The methods for building world models have only recently become practical and have not yet been applied in this context. The proposed route navigation methodology is newly developed at Sussex and is based on insights of insect behaviour only recently observed. Increased knowledge of route navigation will be of interest to engineers and biologists. Parsimonious route-following algorithms will be of use in situations where an agent must reliably navigate between two locations, such as a robotic courier or search-and-rescue robot. Our algorithms also have potential broader applications such as improving guidance aids for the visually-impaired. Biologists and the wider academic community will be able to use the tools developed to gain an understanding of the visual input during behavioural experiments leading to a deeper understanding of target systems. There is specific current interest from Rothamsted Agricultural Institute who are interested in how changes in flight patterns affect visual input and navigational efficacy of honeybee foragers from colonies affected by factors like pesticides or at risk of colony collapse disorder.

我们的总体目标是通过复杂的自然环境开发长距离基于路线的视觉导航算法。尽管自主导航取得了最新进展，特别是在基于地图的同时定位和制图（SLAM）方面，但引导通过非结构化自然地形返回目标位置的问题仍然是一个悬而未决的问题和活跃的研究领域。尽管它们的大脑很小，传感器分辨率低，但昆虫在这样的环境中导航的性能水平超过了最先进的机器人算法。因此，从昆虫身上获得灵感是很自然的。在机器人技术中，生物启发的导航模型已经有了历史，但昆虫行为的已知组成部分尚未被纳入工程解决方案。与大多数现代机器人方法相比，在两个位置之间导航，昆虫，使用程序路线知识，而不是心理地图。路线导航的一个重要特征是，智能体不需要知道它在每一点的位置（在认知地图中定位自己的意义上），而是它应该做什么。昆虫通过其与生俱来的行为适应性为导航算法提供了进一步的灵感，这些行为适应性简化了在非结构化、杂乱环境中的导航，其中一个目标是开发捕获昆虫归巢策略的优雅和期望特性-鲁棒性的导航算法（面对自然环境的变化），吝啬（机制和视觉编码），学习速度（昆虫必须从第一次旅行中学习）和功效（昆虫觅食的简单尺度）。在此之前，我们将把当前关于昆虫行为的见解与新技术结合起来，使我们能够从觅食昆虫的角度重新创建视觉输入。这将为生物学家带来新的工具，并增加我们对昆虫导航的理解。为了实现这些目标，我们的工作包将是：WP 1开发用于重建大规模自然环境的工具。我们将调整现有的全景摄像机系统，使觅食蜜蜂所经历的视觉输入重建。同样，我们将采用新的计算机视觉方法，使我们能够建立蚂蚁杂乱栖息地的世界模型。使用WP 1中开发的世界模型，我们将研究不同的视觉场景编码方式的稳定性和性能WP 3自主路线导航算法。我们将测试一个最近开发的路线导航模型，并增强它在自然环境中的鲁棒性能。苏塞克斯大学刚刚开发出全景摄像系统。构建世界模型的方法只是最近才变得实用，尚未在此背景下应用。拟议的路线导航方法是新开发的苏塞克斯和昆虫行为的见解，最近才观察到的基础上。增加路线导航的知识将引起工程师和生物学家的兴趣。在智能体必须在两个位置之间可靠地导航的情况下，例如机器人信使或搜索和救援机器人，将使用简约的路线跟踪算法。我们的算法也有潜在的更广泛的应用，如改善视觉障碍的指导援助。生物学家和更广泛的学术界将能够使用开发的工具来了解行为实验期间的视觉输入，从而更深入地了解目标系统。目前，Rothamsted农业研究所对飞行模式的变化如何影响蜜蜂觅食者的视觉输入和导航功效感兴趣，这些蜜蜂觅食者来自受杀虫剂等因素影响的殖民地或有殖民地崩溃障碍的风险。

项目成果

Holistic visual encoding of ant-like routes: Navigation without waypoints

• DOI: 10.1177/1059712310395410
• 发表时间: 2011-02-01
• 期刊: ADAPTIVE BEHAVIOR
• 影响因子: 1.6
• 作者: Baddeley, Bart;Graham, Paul;Husbands, Philip
• 通讯作者: Husbands, Philip

Metaheuristic approaches to tool selection optimisation

• DOI: 10.1145/2330163.2330313
• 发表时间: 2012-07
• 作者: Alexander W. Churchill;P. Husbands;Andrew O. Philippides

A model of ant route navigation driven by scene familiarity.

• DOI: 10.1371/journal.pcbi.1002336
• 发表时间: 2012-01
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Baddeley B;Graham P;Husbands P;Philippides A
• 通讯作者: Philippides A

A neural network based holistic model of ant route navigation

基于神经网络的蚂蚁路径导航整体模型

• DOI: 10.1186/1471-2202-13-s1-o1
• 发表时间: 2012
• 期刊: BMC Neuroscience
• 影响因子: 2.4
• 作者: Baddeley B