A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

Insect-inspired navigation: Smart tricks from small brains

Small-brained insects are expert at many tasks that are currently difficult for robots, but especially in the speed and robustness of their learning abilities. In contrast to AI methods which generally take long times to train and large amounts of labelled data, insects are rapid learners of visual and olfactory information and are capable of long distance navigation, exploration and spatial learning. What if we could give robots these abilities, by mimicking the sensors, circuits and behaviours of insects? This is the goal of the Brains on Board project (brainsonboard.co.uk). In this talk, we will discuss the Brains on Board project and our work on insect-inspired visual navigation in particular. The use of visual information for navigation is a universal strategy for sighted animals, amongst whom ants are particular experts despite have small brains and low-resolution vision [1]. To understand how they achieve this, we combine behavioural experiments with modelling and robotics to show how ants directly acquire and use task-specific information through specialised sensors, brains and behaviours, enabling complex behaviour to emerge without complex processing. In this spirit, we will show that an agent – insect or robot – can robustly navigate without ever knowing where it is, without specifying when or what it should learn, nor requiring it to recognise specific objects, places routes or maps. This leads to an algorithm in which visual information specifies actions not locations in which route navigation is recast as a search for familiar views allowing routes through visually complex worlds to be encoded by a single layer artificial neural network (ANN) after a single training run with only low resolution vision [2]. As well as meaning that the algorithms are plausible in terms of memory load and computation for a small-brained insect, it also makes them very well-suited to a small, power-efficient, robot. We thus demonstrate that this algorithm, with all computation performed on a small low-power robot, is capable of delivering reliable direction information along outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain) [3]. Indeed, routes can be precisely recapitulated and we show that the required computation does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than the compact representation losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing. [1] Shettleworth, S. (2010) Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences 14 (11):477-481 [2] Baddeley, B., Graham, P., Husbands, P., & Philippides, A. (2012). A model of ant route navigation driven by scene familiarity. PLoS computational biology, 8(1), e1002336. [3] Knight, J, Sakhapov, D., Domcsek, A., Dewar, A., Graham, P., Nowotny, T., Philippides, A. (2019) Insect-Inspired Visual Navigation On-Board an Autonomous Robot: Real-World Routes Encoded in a Single Layer Network. Proc. Artificial Life 19. In Press

A situated and embodied model of ant route navigation

This abstract summarises a model of route navigation inspired by the behaviour of ants presented fully in Baddeley et al. (2012). The ant's embodiment coupled with an innate scanning behaviour means that robust route navigation can be achieved by a parsimonious biologically plausible algorithm

How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation

The visual systems of animals have to provide information to guide behaviour and the informational requirements of an animal’s behavioural repertoire are often reflected in its sensory system. For insects, this is often evident in the optical array of the compound eye. One behaviour that insects share with many animals is the use of learnt visual information for navigation. As ants are expert visual navigators it may be that their vision is optimised for navigation. Here we take a computational approach in asking how the details of the optical array influence the informational content of scenes used in simple view matching strategies for orientation. We find that robust orientation is best achieved with low-resolution visual information and a large field of view, similar to the optical properties seen for many ant species. A lower resolution allows for a trade-off between specificity and generalisation for stored views. Additionally, our simulations show that orientation performance increases if different portions of the visual field are considered as discrete visual sensors, each giving an independent directional estimate. This suggests that ants might benefit by processing information from their two eyes independently

Vision for navigation: what can we learn from ants?

The visual systems of all animals are used to provide information that can guide behaviour. In some cases insects demonstrate particularly impressive visually-guided behaviour and then we might reasonably ask how the low-resolution vision and limited neural resources of insects are tuned to particular behavioural strategies. Such questions are of interest to both biologists and to engineers seeking to emulate insectlevel performance with lightweight hardware. One behaviour that insects share with many animals is the use of learnt visual information for navigation. Desert ants, in particular, are expert visual navigators. Across their foraging life, ants can learn long idiosyncratic foraging routes. What's more, these routes are learnt quickly and the visual cues that define them can be implemented for guidance independently of other social or personal information. Here we review the style of visual navigation in solitary foraging ants and consider the physiological mechanisms that underpin it. Our perspective is to consider that robust navigation comes from the optimal interaction between behavioural strategy, visual mechanisms and neural hardware.We consider each of these in turn, highlighting the value of ant-like mechanisms in biomimetic endeavours

Individual foraging careers of the Jack Jumper ant, Myrmecia croslandi

A solitary foraging ant needs to rely exclusively on her navigational skill set to successfully navigate to and from goals such as the nest and food sites. Of interest is how ants are able to acquire this navigational information at a young age, before they become experienced workers and how this eventually shapes them into becoming efficient foragers. Ants of Myrmecia croslandi are highly visual, solitary foragers that exhibit no evidence of chemical trail following or recruitment. Therefore, a forager leaving the nest for the first time, must do so completely on her own, first by deciding where to go and second by utilising the information she has acquired from the environment to journey between sites. By identifying and individually following ants, I demonstrate that ants exhibit highly individual behaviour in most tasks, from early learning, and daily foraging to navigating from unfamiliar locations. First, I document the spatial and temporal variation in individual foraging behaviour at two nests of M. croslandi over a two-year period. Ants can take variable routes to the same food site and travel the longest distance when they forage on trees. Individual ants depart the nest at different times and a few ants perform multiple trips per day. Surprisingly, not a single ant foraged on consecutive days. By examining the behaviour of inexperienced ants at the nest, I provide a detailed analysis of the learning walks of M. croslandi. Most learning walks take place in the morning with a narrow time window separating the first two learning walks. There are no common bearing or gaze directions between ants, however, (a) in subsequent walks ants always explore directions that they have not previously visited and (b) ants engage in a systematic, saccadic scanning behaviour. I also discuss the significant differences between learning walks of M. croslandi and those previously studied in two other ant species, especially in relation the ‘turn back and look’ behaviour. In displacement experiments, I provide supporting evidence of a quick scanning behaviour that occurs as soon as ants are released. I examine the effect of a conflict in navigational information on successful homing by comparing full and zero vector ants. Zero vector ants are significantly better at navigating home, especially when released at unfamiliar sites. With the aid of the extensive individual foraging histories available to me, I show how in most cases, scene familiarity plays a role in driving ants home from unfamiliar displacement locations and discuss in detail behaviours that are exceptions to this. Finally, I provide the first evidence of the use of artificial landmarks near the nest in this species, which increases the accuracy with which ants pinpoint the nest entrance, even though they do not appear to make use of such landmarks in the wild and discuss my findings in relation to other ants. I also document the occurrence of re-orientation walks in response to an altered visual environment which show that ants are more directed as a result of re-learning

How Ants Use Vision When Homing Backward

Ants can navigate over long distances between their nest and food sites using visual cues [1, 2]. Recent studies show that this capacity is undiminished when walking backward while dragging a heavy food item [3, 4, 5]. This challenges the idea that ants use egocentric visual memories of the scene for guidance [1, 2, 6]. Can ants use their visual memories of the terrestrial cues when going backward? Our results suggest that ants do not adjust their direction of travel based on the perceived scene while going backward. Instead, they maintain a straight direction using their celestial compass. This direction can be dictated by their path integrator [5] but can also be set using terrestrial visual cues after a forward peek. If the food item is too heavy to enable body rotations, ants moving backward drop their food on occasion, rotate and walk a few steps forward, return to the food, and drag it backward in a now-corrected direction defined by terrestrial cues. Furthermore, we show that ants can maintain their direction of travel independently of their body orientation. It thus appears that egocentric retinal alignment is required for visual scene recognition, but ants can translate this acquired directional information into a holonomic frame of reference, which enables them to decouple their travel direction from their body orientation and hence navigate backward. This reveals substantial flexibility and communication between different types of navigational information: from terrestrial to celestial cues and from egocentric to holonomic directional memories

How variation in head pitch could affect image matching algorithms for ant navigation

Desert ants are a model system for animal navigation, using visual memory to follow long routes across both sparse and cluttered environments. Most accounts of this behaviour assume retinotopic image matching, e.g. recovering heading direction by finding a minimum in the image difference function as the viewpoint rotates. But most models neglect the potential image distortion that could result from unstable head motion. We report that for ants running across a short section of natural substrate, the head pitch varies substantially: by over 20 degrees with no load; and 60 degrees when carrying a large food item. There is no evidence of head stabilisation. Using a realistic simulation of the ant’s visual world, we demonstrate that this range of head pitch significantly degrades image matching. The effect of pitch variation can be ameliorated by a memory bank of densely sampled along a route so that an image sufficiently similar in pitch and location is available for comparison. However, with large pitch disturbance, inappropriate memories sampled at distant locations are often recalled and navigation along a route can be adversely affected. Ignoring images obtained at extreme pitches, or averaging images over several pitches, does not significantly improve performance

Morphogenetic Engineering For Evolving Ant Colony Pheromone Communication

This research investigates methods for evolving swarm communication in a simulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn pheromone communication autonomously. Building on previous literature on pheromone communication, this research applies evolution to adjust the topology and weights of an artificial neural network which controls the ant behaviour. Comparison of performance is made between a hard-coded benchmark algorithm, a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was successfully evolved to achieve the task objective, to collect food and return it to the nest

Swarm Communication by Evolutionary Algorithms

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This research has applied evolutionary algorithms to evolve swarm communication. Controllers were evolved for colonies of artificial simulated ants during a food foriaging task which communicate using pheromone. Neuroevolution enables both weights and the topology of the artificial neural networks to be optimized for food foriaging. The developed model results in evolution of ants which communicate using pheromone trails. The ants successfully collect and return food to the nest. The controller has evolved to adjust the strength of pheromone which provides a signal to guide the direction of other ants in the colony by hill climbing strategy. A single ANN controller for ant direction successfully evolved which exhibits many separate skills including food search, pheromone following, food collection and retrieval to the nest