Owen Holland

Ant-based load balancing in telecommunications networks

This article describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess of traffic can become congested, causing calls to be lost. In addition to calls, the network also supports a population of simple mobile agents with behaviors modeled on the trail-laying abilities of ants. The ants move across the network between randomly chosen pairs of nodes; as they move, they deposit simulated pheromone as a function of their distance from their source node and the congestion encountered on their journey. They select their path at each intermediate node according to the distribution of simulated pheromone at each node. Calls between nodes are routed as a function of the pheromone distributions at each intermediate node. The performance of the network is measured by the proportion of calls that are lost. The results of using ant-based control (ABC) are compared with those achieved by using fixed shortest-path routes, and also those achieved by using an alternative algorithmically based type of mobile agent previously proposed for use in network management. The ABC system is shown to result in fewer call failures than the other methods, while exhibiting many attractive features of distributed control.

From local actions to global tasks: stigmergy and collective robotics

This paper presents a series of experiments where a group of mobile robots gather 81 randomly distributed objects and cluster them into one pile. Coordination of the agents’ movements is achieved through stigmergy. This principle, originally developed for the description of termite building behaviour, allows indirect communication between agents through sensing and modification of the local environment which determines the agents’ behaviour. The efficiency of the work was measured for groups of one to five robots working together. Group size is a critical factor. The mean time to accomplish the task decreases for one, two, and three robots respectively, then increases again for groups of four and five agents, due to an exponential increase in the number of interactions between robots which are time consuming and may eventually result in the destruction of existing clusters. We compare our results with those reported by Deneubourg et al. (1990) where similar clusters are observed in ant colonies, generated by the probabilistic behaviour of workers.

Stigmergy, self-organization, and sorting in collective robotics

Many structures built by social insects are the outcome of a process of self-organization, in which the repeated actions of the insects interact over time with the changing physical environment to produce a characteristic end state. A major mediating factor is stigmergy, the elicitation of specific environment-changing behaviors by the sensory effects of local environmental changes produced by previous behavior. A typical task involving stigmergic self-organization is brood sorting: Many ant species sort their brood so that items at similar stages of development are grouped together and separated from items at different stages of development. This article examines the operation of stigmergy and self-organization in a homogeneous group of physical robots, in the context of the task of clustering and sorting Frisbees of two different types. Using a behavioral rule set simpler than any yet proposed for ant sorting, and having no capacity for spatial orientation or memory, the robots are able to achieve effective clustering and sorting showing all the signs of self-organization. It is argued that the success of this demonstration is crucially dependent on the exploitation of real-world physics, and that the use of simulation alone to investigate stigmergy may fail to reveal its power as an evolutionary option for collective life forms.

Ant-like agents for load balancing in telecommunications networks

This paper describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between arbitrary nodes; nodes carrying an excess of traffic can become congested , causing calls to fail. In addition to calls, the network also supports a population of simple mobile agents with behaviours modelled on the trail laying abilities of ants. The agents move across the network between arbitrary pairs of nodes, selecting their path at each intermediate node according to the distribution of simulated pheromones at each node. As they move they deposit simulated pheromones as a function of their distance from their source node, and the congestion encountered on their journey. Calls between nodes are routed as a function of the pheromone distributions at each intermediate node. The performance of the network is measured by the proportion of calls which fail. The results are compared with those achieved by using fixed shortest-path routes, and also by using an alternative algorithmically-based type of mobile agent. The ant-based system is shown to drop fewer calls than the other methods, while exhibiting many attractive features of distributed control.

The application of wireless local area network technology to the control of mobile robots

This paper describes a communications and control infrastructure for distributed mobile robotics, which makes use of wireless local area network (WLAN) technology and Internet Protocols (IPs). The use of commercial off-the-shelf (COTS) hardware and software components, and protocols, results in a powerful platform for conducting experiments into collective or co-operative robotics. Standard Transmission Control Protocol/Internet Protocol (TCP/IP) compatible applications programming interfaces (APIs) allow for rapid and straightforward development of applications software. Further, the message bandwidth available from WLAN interfaces (1‐2 Mbits/s) facilitates multi-robot experiments requiring high data rates, for instance in robot vision or navigation. The infrastructure described is equally applicable to teleoperated mobile robots. q 2000 Elsevier Science B.V. All rights reserved.

Beyond swarm intelligence: the UltraSwarm

This paper explores the idea that it may be possible to combine two ideas - UAV flocking, and wireless cluster computing - in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric helicopters is described.

ECCE1: The first of a series of anthropomimetic musculoskeletal upper torsos

The human body was not designed by engineers and the way in which it is built poses enormous control problems. Its complexity challenges the ability of classical control theory to explain human movement as well as the development of human motor skills. It is our working hypothesis that the engineering paradigm for building robots places severe limitations on the kinds of interactions such robots can engage in, on the knowledge they can acquire of their environment, and therefore on the nature of their cognitive engagement with the environment. This paper describes the design of an anthropomimetic humanoid upper torso, ECCE1, built in the context of the ECCEROBOT project. The goal of the project is to use this platform to test hypotheses about human motion as well as to compare its performance with that of humans, whether at the mechanical, behavioural or cognitive level.

Extending particle swarm optimisation via genetic programming

Particle Swarm Optimisers (PSOs) search using a set of interacting particles flying over the fitness landscape. These are typically controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarms best. Here we explore the possibility of evolving optimal force generating equations to control the particles in a PSO using genetic programming.

Architectures for functional imagination

Imagination can be defined broadly as the manipulation of information that is not directly available to an agents sensors. However, the topic of imagination raises representational, physiological, and phenomenological issues that cannot be tackled easily without using the body as a reference point. Within this framework, we define functional imagination as the mechanism that allows an embodied agent to simulate its own actions and their sensory consequences internally, and to extract behavioural benefits from doing so. In this paper, we present five necessary and sufficient requirements for the implementation of functional imagination, as well as a minimal architecture that meets all these criteria. We also present a taxonomy for categorising possible architectures according to their main attributes. Finally, we describe experiments with some simple architectures designed using these principles and implemented on simulated and real robots, including an extremely complex anthropomimetic humanoid.

Toward anthropomimetic robotics: Development, simulation, and control of a musculoskeletal torso

Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, were developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.