Philip Husbands

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.

Unconstrained Evolution and Hard Consequences

Artificial evolution as a design methodology for hardware frees many of the simplifying constraints normally imposed to make design by humans tractable. However, this freedom comes at some cost, and a whole fresh set of issues must be considered. Standard genetic algorithms are not generally appropriate for hardware evolution when the number of components need not be predetermined. The use of simulations is problematic, and robustness in the presence of noise or hardware faults is important. We present theoretical arguments, and illustrate with a physical piece of hardware evolved in the real-world (‘intrinsically evolved’ hardware). A simple asynchronous digital circuit controls a real robot, using a minimal sensorimotor control system of 32 bits of RAM and a few flip-flops to co-ordinate sonar pulses and motor pulses with no further processing. This circuit is tolerant to single-stuck-at faults in the RAM. The methodology is applicable to many types of hardware, including Field-Programmable Gate Arrays (FPGAs).

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

It is known that ants learn long visually guided routes through complex terrain. However, the mechanisms by which visual information is first learned and then used to control a route direction are not well understood. In this article, we propose a parsimonious mechanism for visually guided route following. We investigate whether a simple approach, involving scanning the environment and moving in the direction that appears most familiar, can provide a model of visually guided route learning in ants. We implement view familiarity as a means of navigation by training a classifier to determine whether a given view is part of a route and using the confidence in this classification as a proxy for familiarity. Through the coupling of movement and viewing direction, a familiar view specifies a familiar direction of viewing and thus a familiar movement to make. We show the feasibility of our approach as a model of ant-like route acquisition by learning a series of nontrivial routes through an indoor environment using a large gantry robot equipped with a panoramic camera.

Modeling Cooperative Volume Signaling in a Plexus of Nitric Oxide Synthase-Expressing Neurons

In vertebrate and invertebrate brains, nitric oxide (NO) synthase (NOS) is frequently expressed in extensive meshworks (plexuses) of exceedingly fine fibers. In this paper, we investigate the functional implications of this morphology by modeling NO diffusion in fiber systems of varying fineness and dispersal. Because size severely limits the signaling ability of an NO-producing fiber, the predominance of fine fibers seems paradoxical. Our modeling reveals, however, that cooperation between many fibers of low individual efficacy can generate an extensive and strong volume signal. Importantly, the signal produced by such a system of cooperating dispersed fibers is significantly more homogeneous in both space and time than that produced by fewer larger sources. Signals generated by plexuses of fine fibers are also better centered on the active region and less dependent on their particular branching morphology. We conclude that an ultrafine plexus is configured to target a volume of the brain with a homogeneous volume signal. Moreover, by translating only persistent regional activity into an effective NO volume signal, dispersed sources integrate neural activity over both space and time. In the mammalian cerebral cortex, for example, the NOS plexus would preferentially translate persistent regional increases in neural activity into a signal that targets blood vessels residing in the same region of the cortex, resulting in an increased regional blood flow. We propose that the fineness-dependent properties of volume signals may in part account for the presence of similar NOS plexus morphologies in distantly related animals.

Linked Local Navigation for Visual Route Guidance

Insects are able to navigate reliably between food and nest using only visual information. This behavior has inspired many models of visual landmark guidance, some of which have been tested on autonomous robots. The majority of these models work by comparing the agents current view with a view of the world stored when the agent was at the goal. The region from which agents can successfully reach home is therefore limited to the goals visual locale, that is, the area around the goal where the visual scene is not radically different to the goal position. Ants are known to navigate over large distances using visually guided routes consisting of a series of visual memories. Taking inspiration from such route navigation, we propose a framework for linking together local navigation methods. We implement this framework on a robotic platform and test it in a series of environments in which local navigation methods fail. Finally, we show that the framework is robust to environments of varying complexity.

Evolution Versus Design: Controlling Autonomous Robots

This paper sets out and justifies a methodology for the development of the control systems, or ‘cognitive architectures)) of autonomous mobile robots. It will be argued that the design b y hand of such control systems becomes prohibitively dificult as complexity increases. The alternative approach of artificial evolution is presented. It is argued that the most useful basic building blocks for an evolved cognitive archite(*t ? ure are adaptive noise tolerant neural networks raiher than programs. These networks may be recurrent, and should operate in real time. Evolution should be incremental, using an extended and modified version of genetic algorithms. Time constraints mean that architecture evaluations must be largely done an simulation. Results from a simulation are presented. The pitfalls of simulations compared with reality is discussed, together with the importance of incorporating noise.

An ecosystems model for integrated production planning

Abstract This paper re-evaluates the job-shop scheduling problem by showing how the standard definition is far more restrictive than necessary and by presenting a new technique capable of tackling a highly generalized version of the problem. This technique is based on a massively parallel distributed genetic algorithm and is capable of simultaneously optimizing the process plans of a number of different components, at the same time a near-optimal schedule emerges. Underlying the evolutionary machinery is a specialized feature-based generative process planner.

The View From Elsewhere: Perspectives on ALife Modeling

Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshops eight speakers, plus a section designed to reflect the debates that took place during the workshops discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor.

Spatial, temporal, and modulatory factors affecting GasNet evolvability in a visually guided robotics task

Spatial, temporal, and modulatory factors affecting the evolvability of GasNets — a style of artificial neural network incorporating an analogue of volume signalling — are investigated. The focus of the article is a comparative study of variants of the GasNet, implementing various spatial, temporal, and modulatory constraints, used as control systems in an evolutionary robotics task involving visual discrimination. The results of the study are discussed in the context of related research.

A neural network based holistic model of ant route navigation

The impressive ability of social insects to learn long foraging routes guided by visual information [1] provides proof that robust spatial behaviour can be produced with limited neural resources [2,3]. As such, social insects have become an important model system for understanding the minimal cognitive requirements for navigation [1]. This is a goal shared by biomimetic engineers and those studying animal cognition using a bottom-up approach to the understanding of natural intelligence [4]. Models of visual navigation that have been successful in replicating place homing are dominated by snapshot-type models where a single view of the world as memorized from the goal location is compared to the current view in order to drive a search for the goal [5], for review, see [6]. Snapshot approaches only allow for navigation in the immediate vicinity of the goal however, and do not achieve robust route navigation over longer distances [7]. Here we present a parsimonious model of visually guided route learning that addresses this issue [8]. 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. Our navigation algorithm consists of two phases. The ant first traverses the route using a combination of path integration and obstacle avoidance during which the views used to learn the route are experienced. Subsequently, the ant navigates by visually scanning the world – a behaviour observed in ants in the field – and moving in the direction which is deemed most familiar. As proof of concept, we first determine view familiarity by exhaustive comparison with the set of views experienced during training. In subsequent experiments we train an artificial neural network to perform familiarity discrimination using the training views via the InfoMax algorithm [9]. By utilising the interaction of sensori-motor constraints and observed innate behaviours we show that it is possible to produce robust behaviour using a learnt holistic representation of a route. Furthermore, we show that the model captures the known properties of route navigation in desert ants. These include the ability to learn a route after a single training run and the ability to learn multiple idiosyncratic routes to a single goal. Importantly, navigation is independent of odometric or compass information, does not specify when or what to learn nor separate the routes into sequences of waypoints, so providing proof of concept that route navigation can be achieved without these elements. The algorithm also exhibits both place-search and route navigation with the same mechanism. As such, we believe the model represents the only detailed and complete model of insect route guidance to date.