Insect-inspired visually guided autonomous route navigation through natural environments

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.

The cross-disciplinary work will be of interest to engineers and biologists in academia and industry as well as the general public and schools. It fits within EPSRCs Control Systems and Robot Engineering area and Cross-Disciplinary programme. Academia: Robust outdoor route navigation is an active area of research and our algorithms will be of interest to engineers and computer scientists working on ground-based and airborne guidance. Moreover, tools for reconstructing environments will allow these groups to test out their own models, while adaptation of Dense Scene Reconstruction will interest those working on 3D depth map reconstruction. Similarly, stable visual feature extraction in natural habitats will aid feature selection and data association problems in robotics and computer vision applications more generally. Biologists working on insect visual behaviour will benefit from software tools and data from the project by reconstructing sensory input experienced during their experiments, thus tying sensation to action. We will also produce testable hypotheses on the visual features and algorithms used for navigation. Specifically, Rothamsted Agricultural Institute will use our tools to interpret the effect of altered flight patterns of honeybees from infected colonies. More generally, as the navigational strategies of insects resemble, to a surprising extent, those of animals with much larger brains, a similarity likely to have arisen through convergent evolution of navigational mechanisms; understanding how insects operate and the cognitive processes involved is of interest to neuroscientists and psychologists. Industry: As their interests often align with academics, the novel technologies produced will be of interest to industrial robotic and computer vision applications in natural environments. Insect-inspired control systems are much used in autonomous robotics for autonomous navigation and exploration and in the control of unmanned air vehicles in particular. More generally, route navigation and visual feature selection will impact visual guidance systems (eg for the visually impaired). Finally, the games industry is a major driver of 3D mapping and adaptations of these technologies to large-scale environments will interest them. Through Rothamsted, the application of our work to pollinator flight paths will be of interest to farmers and industry concerned with crop pollination. Agro-chemical industries can assess the impact of altered flight patterns of bees exposed to pesticide (or disease) on the visual information perceived and whether this means, for instance, that they are unable to learn hive position, and plan mitigation strategies accordingly. Beekeepers are always fascinated by how their bees behave and will take great interest in the tools developed and the window they give on the sensory consequences of bee flight. General Public and Schools: The public is fascinated by insects and their lifestyles, and the ways in which we use robots and technology to study them. Moreover, the promise of autonomous travel always sparks interest in the benefits technology can bring to everyday life. Communicating our research will ensure we have an engaged generation who can see the benefits of technology in the study of the natural world. In particular, potential students will see the range of careers available to science students and the links between Biological and Computer Science. They will also see that studying computer science need not result in a career in systems admin, but can involve navigating flying robots and helping understand the brain. The team: The PDRA will gain useful transferable skills and expertise in 3D depth map reconstruction and insect behaviour. Summary: By disseminating our work through high-impact publications and international robotics and biology conferences and hosting specialist workshops, we will impact both UK and international groups.