Research Fellow, Macquarie University
Like Voltron, ants can join together to increase their power. I want this ability for robot swarms.
How do nerve cells network to form a brain? How do ants build bridges to form smooth highways for their traffic? How do competing economic firms interact to form a system of global trade? These are all examples of complex systems; where groups of simple interacting units produce emergent, complex behaviour at the group level. Complex systems are all around us, but they are difficult to study; an individual nerve cell can be removed from a body and observed, but in doing so we have removed the cell from the very system we seek to understand. Our inability to observe the precise behaviour of individual units as they interact with their neighbours is a major impediment to understanding how complex systems function.
I use biological complex systems such as ant colonies, honey bees and slime moulds to circumvent this problem. These systems provide an extremely important resource for studying the properties of complex systems in general, because they allow me to design experiments to uncover the links between the behaviour of individual agents and the behaviour of the entire system. My work is naturally interdisciplinary, involving collaboration with biologists, engineers and mathematicians, and a combination of field work, lab work and computer modelling.
One of my main aims is to understand self-assembly. Self-assembly is the ability of certain species of ants to use simple, local behavioural rules to join their bodies together, spontaneously building structures such as hanging chains that act as rope ladders, pulling chains to roll leaves together to form nests, and bridges that span gaps and act as highways for foraging traffic. Made entirely of ants, these structures allow colonies to perform tasks that are impossible for individuals to do alone.
My research has two aims; 1) developing better models for understanding complex systems, such as tissue development and wound healing, and; 2) developing new control algorithms for swarms of simple robots. Just like the ants, these robot swarms will be able to self-assemble into functional structures in any environment, adapt to environmental changes, and maintain functionality when much of the group is lost or disabled, making them ideal for exploration and search-and-rescue operations. My approach will transform our understanding of other complex systems too, such as in finance (interacting economies), disease (tissue development, protein folding) and technology (autonomous vehicles, nano-assembly).
Abstract: The ability of individual animals to create functional structures by joining together is rare and confined to the social insects. Army ants (Eciton) form collective assemblages out of their own bodies to perform a variety of functions that benefit the entire colony. Here we examine ‟bridges” of linked individuals that are constructed to span gaps in the colony’s foraging trail. How these living structures adjust themselves to varied and changing conditions remains poorly understood. Our field experiments show that the ants continuously modify their bridges, such that these structures lengthen, widen, and change position in response to traffic levels and environmental geometry. Ants initiate bridges where their path deviates from their incoming direction and move the bridges over time to create shortcuts over large gaps. The final position of the structure depended on the intensity of the traffic and the extent of path deviation and was influenced by a cost–benefit trade-off at the colony level, where the benefit of increased foraging trail efficiency was balanced by the cost of removing workers from the foraging pool to form the structure. To examine this trade-off, we quantified the geometric relationship between costs and benefits revealed by our experiments. We then constructed a model to determine the bridge location that maximized foraging rate, which qualitatively matched the observed movement of bridges. Our results highlight how animal self-assemblages can be dynamically modified in response to a group-level cost–benefit trade-off, without any individual unit’s having information on global benefits or costs.
Pub.: 23 Nov '15, Pinned: 25 Aug '17
Abstract: The aggregation phenomenon is very common in numerous activities of social insects, however, it is often their functional aspects that are studied, leaving their mechanisms not so well understood. With the example of chain formation in Œcophylla longinoda, we present the mechanisms responsible for these collective structures. Our experimental results show that a change in the probability that a worker will decide to join or leave a chain is (1) strongly dependent on the number of ants present in the chain and (2) slightly dependent on the presence of a visual stimulus. The determining role of these probabilities is validated with the use of a mathematical model that reproduces the formation and breakup of the chain. Moreover, it predicts other properties of aggregation such as the influence of nest population size.
Pub.: 01 Sep '01, Pinned: 20 Nov '17
Abstract: Robustness and adaptability are central to the functioning of biological systems, from gene networks to animal societies. Yet the mechanisms by which living organisms achieve both stability to perturbations and sensitivity to input are poorly understood. Here, we present an integrated study of a living architecture in which army ants interconnect their bodies to span gaps. We demonstrate that these self-assembled bridges are a highly effective means of maintaining traffic flow over unpredictable terrain. The individual-level rules responsible depend only on locally-estimated traffic intensity and the number of neighbours to which ants are attached within the structure. We employ a parameterized computational model to reveal that bridges are tuned to be maximally stable in the face of regular, periodic fluctuations in traffic. However analysis of the model also suggests that interactions among ants give rise to feedback processes that result in bridges being highly responsive to sudden interruptions in traffic. Subsequent field experiments confirm this prediction and thus the dual nature of stability and flexibility in living bridges. Our study demonstrates the importance of robust and adaptive modular architecture to efficient traffic organisation and reveals general principles regarding the regulation of form in biological self-assemblies.
Pub.: 05 Apr '13, Pinned: 25 Aug '17
Abstract: Ants live in dynamically changing environments, where food sources become depleted and alternative sources appear. Yet most mathematical models of ant foraging assume that the ants' foraging environment is static. Here we describe a mathematical model of ant foraging in a dynamic environment. Our model attempts to explain recent empirical data on dynamic foraging in the Argentine ant Linepithema humile (Mayr). The ants are able to find the shortest path in a Towers of Hanoi maze, a complex network containing 32,768 alternative paths, even when the maze is altered dynamically. We modify existing models developed to explain ant foraging in static environments, to elucidate what possible mechanisms allow the ants to quickly adapt to changes in their foraging environment. Our results suggest that navigation of individual ants based on a combination of one pheromone deposited during foraging and directional information enables the ants to adapt their foraging trails and recreates the experimental results.
Pub.: 12 May '12, Pinned: 21 Nov '17
Abstract: Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems.
Pub.: 10 Jun '16, Pinned: 21 Nov '17
Abstract: The study of collective behaviour aims to understand how individual-level behaviours can lead to complex group-level patterns. Collective behaviour has primarily been studied in animal groups such as colonies of insects, flocks of birds and schools of fish. Although less studied, collective behaviour also occurs in microorganisms. Here, we argue that slime moulds are powerful model systems for solving several outstanding questions in collective behaviour. In particular, slime mould may hold the key to linking individual-level mechanisms to colony-level behaviours. Using well-established principles of collective animal behaviour as a framework, we discuss the extent to which slime mould collectives are comparable to animal groups, and we highlight some potentially fruitful areas for future research.
Pub.: 01 Sep '16, Pinned: 21 Nov '17
Abstract: Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.
Pub.: 16 Aug '14, Pinned: 25 Aug '17
Abstract: In this paper, we present the design of a new structural extension for the e-puck mobile robot. The extension may be used to transform what is traditionally a swarm robotics platform into a self-reconfigurable modular robotic system. We introduce a modified version of a previously developed collective locomotion algorithm and present new experimental results across three different themes. We begin by investigating how the performance of the collective locomotion algorithm is affected by the size and shape of the robotic structures involved, examining structures containing up to nine modules. Without alteration to the underlying algorithm, we then analyse the implicit self-assembling and self-reconfiguring capabilities of the system and show that the novel use of ‘virtual sensors’ can significantly improve performance. Finally, by examining a form of environment driven self-reconfiguration, we observe the behaviour of the system in a more complex environment. We conclude that the modular e-puck extension represents a viable platform for investigating collective locomotion, self-assembly and self-reconfiguration.
Pub.: 09 May '13, Pinned: 22 Nov '17
Abstract: The optimal design of sprinkler irrigation systems is a complicated nonlinear programming problem that is related to the performance of the system and meanwhile an economic problem to farmers in developing countries. Ant colony optimization (ACO), a meta-heuristic algorithm with the strategies inspired by foraging ants, was considered. Exactly an Ant Cycle System was proposed to solve this problem. The performance of ACO was compared to that of Genetic Algorithm (GA), and the optimal results were further validated by field tests on four small-scale irrigation systems. In the optimization model, the objective function was minimizing the specific energy consumption subject to the constraints of pipe diameters, number of sprinklers and working pressure of the end sprinkler along the pipeline and pump-pipeline cooperation conditions. In the design of ACO, head loss between adjacent sprinklers was introduced in the heuristic function to represent the distance between two cities in a Travelling Salesman Problem (TSP). And the fitness composed of the specific energy consumption dealt with penalty function was taken instead of the total length of a route in the pheromone updating. The results indicate that the specific energy consumption has been decreased in average by 12.45 % through ACO, 10.27 % through GA and 11.27 % from field tests compared to that in the initial configurations with irrigation uniformities higher than 75 % in the field tests. ACO implementation outperforms genetic algorithm in efficiency and reliability especially in larger systems. The ACO may provide a promising approach for the optimization of irrigation systems.
Pub.: 04 Mar '15, Pinned: 22 Nov '17
Abstract: Multiple Objective Optimisation is a fast growing area of research, and consequently several Ant Colony Optimisation approaches have been proposed for a variety of these problems. In this paper, a taxonomy for Multiple Objective Ant Colony Optimisation algorithms is proposed and many existing approaches are reviewed and described using the taxonomy. The taxonomy offers guidelines for the development and use of Multiple Objective Ant Colony Optimisation algorithms.
Pub.: 09 Dec '08, Pinned: 22 Nov '17
Abstract: The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.
Pub.: 26 May '17, Pinned: 22 Nov '17
Abstract: Why does a single fire ant Solenopsis invicta struggle in water, whereas a group can float effortlessly for days? We use time-lapse photography to investigate how fire ants S. invicta link their bodies together to build waterproof rafts. Although water repellency in nature has been previously viewed as a static material property of plant leaves and insect cuticles, we here demonstrate a self-assembled hydrophobic surface. We find that ants can considerably enhance their water repellency by linking their bodies together, a process analogous to the weaving of a waterproof fabric. We present a model for the rate of raft construction based on observations of ant trajectories atop the raft. Central to the construction process is the trapping of ants at the raft edge by their neighbors, suggesting that some "cooperative" behaviors may rely upon coercion.
Pub.: 27 Apr '11, Pinned: 21 Nov '17