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

基本信息

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

项目摘要

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.

我们的总体目标是开发通过复杂自然环境的基于长途路线的视觉导航算法。尽管自动导航最近取得了进步，尤其是在基于地图的同时定位和映射（SLAM）中，但通过非结构化的自然地形指导回到目标位置的问题是一个空旷的问题和积极的研究领域。尽管它们的大脑和嘈杂的低分辨率传感器，但昆虫仍在以一定程度的性能上导航，以超过最先进的机器人算法。因此，从昆虫中汲取灵感是很自然的。机器人技术中有生物启发的导航模型的历史，但是尚未将昆虫行为的组成部分纳入工程解决方案。与大多数现代机器人方法相反，在两个位置（昆虫）之间导航，使用程序性路线知识而不是心理图。路线导航的一个重要特征是，代理不需要知道它在每个点的位置（在认知图中本身定位的意义），而是应该做的。昆虫通过其天生的行为适应来为导航算法提供进一步的灵感，从而简化了通过非结构化的，混乱的环境进行导航。一个目标是开发导航算法，以捕获昆虫居住策略的优雅和理想特性的导航算法 - 稳健性 - 稳健性（面对自然环境变化），从自然环境中进行启发（Queptimention and Inteldimention and Inteldimentions of Enchientions for Inteldimention fornection fornection fornection），并具有昆虫的编码，并有效地编码）（昆虫觅食的简单比例）。在此之前，我们将通过新技术将当前有关昆虫行为的见解汇总在一起，从而使我们从觅食昆虫的角度重新创建视觉输入。这将为生物学家提供新的工具，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：WP1开发用于重建大型自然环境的工具。我们将调整现有的全景相机系统，以重建通过觅食蜜蜂经历的视觉输入。同样，我们将调整新的计算机视觉方法，使我们能够建立对通道最佳视觉编码的ANTSWP2调查的杂乱栖息地的世界模型。使用WP1中开发的世界模型，我们将研究编码Visual SceneWp3自主路由导航算法的不同方式的稳定性和性能。我们将测试一个最近开发的路线导航模型，并在该项目中的自然环境方法中增强其在自然环境中的稳健性能是新颖而及时的。全景相机系统刚刚在苏塞克斯开发。建立世界模型的方法直到最近才变得实用，并且在这种情况下还没有应用。所提出的路线导航方法是在苏塞克斯新开发的，是基于昆虫行为的见解，直到最近才观察到。工程师和生物学家对路线导航的知识增加将引起人们的关注。在代理必须可靠地在两个位置（例如机器人快递或搜索机器人）之间可靠地导航的情况下，将使用偏爱的路线跟随算法。我们的算法还具有更广泛的应用，例如改善视觉障碍的指导辅助工具。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，从而深入了解目标系统。 Rothamsted农业研究所目前具有特定的兴趣，他们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，而蜜蜂觅食者来自受农药或有菌落崩溃障碍的风险影响的殖民地。