Bayesian issues in ant navigation

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics

Abstract

Our brains have to deal with ambiguity and uncertainty, and an increasingly popular explanation of how they do so is based on Bayesian reasoning. In essence, this says we estimate the probability of a certain state of affairs (such as 'I am at home') on the basis of both current sensory inputs ('This looks like my house') and prior expectations ('Given my starting location, and the speed I was travelling, I wouldn't expect to be home yet'). Bayes theorem tells us how we should combine these factors to obtain the best estimate of our current state. But is this form of reasoning universal? An ideal way to investigate this issue is to look at 'simple' animals that have to solve analogous problems. And an effective way to test our understanding of what these animals do is to implement and test our hypotheses in robot models that operate in the same sensory environment. A clear example of an animal solving such problems is found in desert ants, who forage individually and without the use of chemical trails, yet can efficiently relocate their nest or a food source over long distances in barren or complex environments. Recent studies have shown that ants can individually learn and recall specific routes through cluttered environments that force detours and prevent the use of distant landmarks. Ant navigation depends on two main mechanisms: they can keep track of how far they have moved and in which direction from the nest and continuously update a vector that points back home; and they can recognise familiar visual surroundings and use these to determine which way to go. Do they integrate these cues in an optimal fashion? What if one or other cue is more or less variable? Can they use one of these cues to disambiguate the other? We can make the investigation of these issues rigorous and quantitative by drawing on methods developed for robot navigation. We will first determine what ants actually see as they develop new routes, by following ants as they forage, and capturing images from the ant's eye point of view. We will feed this information into algorithms that should be able to learn a map of the area. We can systematically vary the type of information available, its reliability, and the computational methods used to update the map, and compare the performance to ants. Further experiments to see what the ants do when the same variables are manipulated will serve to evaluate the models. Finally, the models will also be tested in the real world by implementing them on a small robot able to navigate in the ant environment.

Technical Summary

We want to answer the question: do all animals have Bayesian brains? Ant navigation is an ideal test case, as a complex, cognitive capability displayed by a small brained animal, and as a problem for which there is well developed Bayesian theory in robotics. We will first gather rich data about the sensory experiences of ants navigating in their natural habitat. An ant nest will be moved to a new area and access points restricted. Ants will be followed as they develop new foraging routes. An image database will be collected and used to reconstruct a virtual ant environment for use in modelling. We will then test how different forms of Bayesian mapping algorithms, in particular topological forms of visual Simultaneous Localisation and Mapping (SLAM), perform when provided with the same input as the ant. Due to the high visual similarity of ant environments, this is not a trivial technical problem, and we will be able to use the virtual environment to selectively introduce different forms of uncertainty and noise and make quantitative predictions for ant behaviour. The search densities for ants returning to a nest under path integration, visual guidance or both can be measured, and Bayesian cue integration theory can be used to assess if ants optimally combine the cues, or alternate between them, and if either strategy is altered as the variance of one or other cue is increased. Ants following routes should face the problem of visual aliasing, and we will use the predictions of Bayesian filtering algorithms to evaluate the extent to which they use prior expectation to disambiguate cues. Revised models based on these results will be implemented on a small robot (surveyor SRV-1 black-fin) which will carry a panoramic camera and polarised light compass sensor, and be deployed in the real ant environment to obtain to obtain real-world evaluation.

Planned Impact

Although the project outlined here is primarily basic science, there are several ways in which it may have broader societal and economic impact. The methodologies proposed in our work have implications for science policy linked to public views on animal experimentation. We are proposing two ways in which questions about behaviour, cognition and intelligence can be answered, or at least refined, prior to undertaking experiments on vertebrates (including humans), allowing such experiments to be reduced or made more productive. The first is the use of insects as our target system, and their study in ecologically relevant contexts. The task of navigation is broadly similar across a wide variety of animals, and there is likely to be at least some convergence in the underlying algorithms. The second is the use of well-grounded modelling to test hypotheses and produce explicit predictions in advance of experimentation. This allows refinement of the paradigms to carry out only the most informative manipulations. In connection with this issue, we believe important socio-cultural insights can be provoked by the comparison of human behaviour to very different species, promoting deeper ecological appreciation. From another perspective, there are interesting philosophical and ethical considerations raised by mechanistic explanations of behaviour that draw strong parallels between animals and robots. We have a range of connections to public dialogue on these issues as outlined in our attached 'pathway to impact'. Another more practical area in which the research should have some impact is in commercial applications of robotics. Robotics is widely predicted to have an increasingly important economic role. Navigation is one of the fundamental capabilities required for autonomous robots to be employed in areas such as exploration of hazardous environments and disaster areas; yet existing methods are not effective, or not efficient, in unstructured environments. Understanding how the ant solves this problem, and building a robot system to test proposed mechanisms in the same conditions as the ant, could lead directly to new solutions with potential for commercial development. We have a direct route for taking any such developments forward through Informatics Ventures, a scheme funded by Scottish Enterprise and the European regional development fund which provides networking and connections to sources of venture capital for informatics researchers. Finally, we anticipate direct social impact through science communication activities. Our work, connecting biology and robotics, has consistently attracted media interest. It has also been used as the basis of talks and workshops at science festivals, and in events for school children to promote recruitment into science and engineering study. We plan to continue our direct involvement in such activities as outlined in the 'pathway to impact'.

Publications

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Ardin P (2015) How variation in head pitch could affect image matching algorithms for ant navigation. in Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology

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Cheung A (2014) Still no convincing evidence for cognitive map use by honeybees. in Proceedings of the National Academy of Sciences of the United States of America

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Graham P (2015) Insect navigation: do ants live in the now? in The Journal of experimental biology

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Kodzhabashev A (2015) Biomimetic and Biohybrid Systems

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Mangan, M. (2011) How many memories do ants need for route navigation? in Conference Poster

 
Description We have tracked ants (using differential GPS and video) throughout their entire foraging life, demonstrating that they are able to acquire and recall visual route memories with only one or two trials. We have obtained experimental results demonstrating ants combine path integration with visual memory cues in a Bayesian optimal manner, but do so using a proxy estimate for uncertainty. We have built a virtual reality simulation of the real ant environment and used to assess a range of navigation models, including the first insect navigation model to be closely based on known neural circuits. We have also successfully tested the algorithms on a robot platform in the field. A closer look at the visual information available to ants has led to development of a novel skyline sensor which we have demonstrated can be used for robot navigation and is the subject of a submitted patent.
Exploitation Route Our discovery that ants use a proxy for weighting cues relative to their uncertainty has wide implications for the Bayesian brain hypothesis in other animals and humans. The connection we have drawn between navigation algorithms and the neural mechanisms of memory in insects can be taken forward in both fields. The results have potential application as low-cost and low-computation sensory methods that can be deployed on robots and autonomous vehicles.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Transport

 
Description Our field analysis has inspired the development of a novel sensor for robots resulting in a robot conference publication, patent application (pending). Commercialisation has already progressed through a Scottish Universities accelerator program with likely avenues being a university backed spin out and/or licence agreement. The results have also been highlighted in several media reports, with a news feature on the BBC, and a documentary shown in north America.
Sector Aerospace, Defence and Marine,Education,Manufacturing, including Industrial Biotechology,Transport
Impact Types Cultural,Economic

 
Description Research Council grant
Amount £698,021 (GBP)
Funding ID EP/M008479/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 02/2015 
End 02/2018
 
Description SICSA Elevate Accelerator Program
Amount £15,000 (GBP)
Organisation SICSA Scottish Informatics and Computer Science Alliance 
Sector Academic/University
Country United Kingdom
Start 05/2014 
End 08/2014
 
Title 3D ant world 
Description This is a 3D virtual world based directly on our field site in Spain which can be used to provide input to simulated ants under control of different algorithms 
Type Of Material Computer model/algorithm 
Year Produced 2014 
Provided To Others? Yes  
Impact The model has been used to test both theoretical and biologically based algorithms. 
URL http://www.insectvision.org/walking-insects/antnavigationchallenge
 
Description Hosted a PhD student from QMUL at University of Edinburgh leading to publication in PLoS Computational Biology 
Organisation Queen Mary University of London
Country United Kingdom 
Sector Academic/University 
PI Contribution Hosted a PhD student from QMUL at University of Edinburgh leading to publication in PLoS Computational Biology
Collaborator Contribution Hosted a PhD student from QMUL at University of Edinburgh leading to publication in PLoS Computational Biology
Impact Publication: Using an insect mushroom body circuit to encode route memory in complex natural environments P Ardin, F Peng, M Mangan, K Lagogiannis, B Webb PLoS computational biology 12 (2), e1004683
Start Year 2013
 
Description Multi-group field work 
Organisation German Aerospace Centre (DLR)
Country Germany 
Sector Public 
PI Contribution Each year our research team would make arrangements for field work in Seville, Spain. This would include planning meetings, invitations to collaborators, and all logistics.
Collaborator Contribution Partners from DLR and Univ of Sussex brought expertise in field work, experimental design, and specific equipment such as a laser scanners used to map the ant habitat.
Impact The collaboration is multi-disciplinary and has resulted in a series of publications as well as future publications and data-sets.
Start Year 2011
 
Description Multi-group field work 
Organisation Spanish National Research Council (CSIC)
Country Spain 
Sector Public 
PI Contribution Each year our research team would make arrangements for field work in Seville, Spain. This would include planning meetings, invitations to collaborators, and all logistics.
Collaborator Contribution Partners from DLR and Univ of Sussex brought expertise in field work, experimental design, and specific equipment such as a laser scanners used to map the ant habitat.
Impact The collaboration is multi-disciplinary and has resulted in a series of publications as well as future publications and data-sets.
Start Year 2011
 
Description Multi-group field work 
Organisation University of Sussex
Department School of Life Sciences Sussex
Country United Kingdom 
Sector Academic/University 
PI Contribution Each year our research team would make arrangements for field work in Seville, Spain. This would include planning meetings, invitations to collaborators, and all logistics.
Collaborator Contribution Partners from DLR and Univ of Sussex brought expertise in field work, experimental design, and specific equipment such as a laser scanners used to map the ant habitat.
Impact The collaboration is multi-disciplinary and has resulted in a series of publications as well as future publications and data-sets.
Start Year 2011
 
Description Partnership with Prof Viktor Gruev of University of Washington to develop novel robot sensor on a newly funded EPSRC grant 
Organisation Washington University in St Louis
Country United States 
Sector Academic/University 
PI Contribution We actively sought collaboration with Prof Viktor Gruev as he is a world leader in development of cameras sensitive to polarised light. As part of the project we shall test the camera in the field and apply it to robot systems to identify crucial cues and most effective hueristics.
Collaborator Contribution Prof Gruev's team shall develop a custom camera in collaboration with the research team that shall form the basis of a novel robot sensor. In collaboration with the feedback from our group a design for manufacture of a commercially exploitable sesnsor shall be developed.
Impact Grant commences in 2015
Start Year 2014
 
Description Appearance in Animal Navigation Documentary shown on North American TV 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Increased wider awareness of our research. Helped establish Dr Mangan as a leader in the field.

Increased interest in my work both from academics and general public.
Year(s) Of Engagement Activity 2013
 
Description Media Coverage (BBC) 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Online article plus video interview with the BBC Science Editor. Appeared on the BBC main page and was covered by other media outlets as a result.

Increase in interest from international media and general public.
Year(s) Of Engagement Activity 2014
URL http://www.bbc.co.uk/news/science-environment-26222542
 
Description Talk at Midlothian Science Festival 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? Yes
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact Talk was well received with questions throughout and following.

Follow up request to speak at future events.
Year(s) Of Engagement Activity 2014