Abstract. Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and e ectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three di erent types of communication for three di erent tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can signi cantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as e ective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.

This paper 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 behaviours modelled 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 pheromones 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 the distribution of simulated pheromones 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 which are lost. The results of using the ant-based control (ABC) are compared with th...

We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a "virtual pheromone," implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform.

An issue central to the navigation problem is memory. Traditional systems build symbolic maps of the world for navigational reference. Reactive methods, in contrast, eliminate or minimize the use of memory. These reactive techniques have been remarkably successful at solving a wide range of navigational problems. Some problems, however, still present a challenge to reactive strategies, (box canyons for example). The addition of a local spatial memory allows a robot to avoid areas that have already been visited. "Avoiding the past" offers a solution to the box canyon and other navigational problems. An avoid-past strategy has been implemented using a spatial memory within a schema-based motor control model. Experiments have produced promising results in simulation and on mobile robots. I. Introduction Reactive robotic control systems have emerged as an answer to many of the problems which arise in navigation across unmapped terrain. Reactive systems are characterized by tight sensor t...

We are pursuing techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance,reconnaissance, hazard detection, and path finding. Using the biologically inspired notion of "virtual pheromone" messaging, we describe how many coordinated activities can be accomplished without centralized control. By virtue of this simple messaging scheme, a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface.\ We further describe a set of logical primitives for controlling the flow of virtual pheromone messages throughout the robot swarm. These enable the design of complex group behaviors mediated by messages exchanged between neighboring robots.

Autonomous multi-entity systems are plentiful in natural and artificial worlds. Many systems have been studied in depth and some models of them have been built as computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article surveys research work done along this direction and proposes autonomy oriented computing (AOC) as a paradigm to describe systems for solving hard computational problems and for characterizing the behaviors of a complex system. AOC differs from major complex system related studies such as artificial life, simulated evolution, and multi-agent systems in that AOC is not just intended to replicate complex behavior, emulate evolution, or coordinate the functioning of many interacting agents. AOC emphasizes the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal. Through examining implemented applications, we identify three main approaches to AOC. Specifically, we provide a detailed description of the AOC framework with formal definitions of essential constructs and their interrelationships, including the notions of emergent autonomy, self-organization, and the interactions among entities and environment.

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Examples of autonomous multi-entity systems are plentiful, both in the natural and artificial worlds. Many systems have been studied in depth and some models of these have been built in computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article proposes autonomy oriented computation (AOC) as a complementary paradigm for solving hard computational problems and for characterizing the behaviors of a complex system.

This paper proposes a novel data clustering algorithm, coined ‘cellular ants’, which combines principles of cellular automata and ant colony optimization algorithms to group similar multidimensional data objects within a two-dimensional grid. The proposed method assigns data objects to unique ants, which actively move around, leave pheromones and follow trails of similar ants. Cellular automata principles based on simple, discrete neighborhood densities determine an ant’s directional movements, so that clusters emerge. The novel concept of ‘positional swapping ’ organizes these clusters internally based on multi-dimensional data value similarity. As a result, shared cluster borders in grid space contain data objects that are nearby in parameter space. This method is algorithmically simple, as it is based on a few user-chosen variables and uses fixed discrete values instead of probability algorithms. This clustering technique is evaluated using several datasets, while its methodology and computational performance is compared to similar approaches. 1.

Examples of autonomous multi-entity systems are plentiful, both in the natural and arti cial worlds. Many systems have been studied in depth and some models of these have been built in computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article introduces the concept of autonomy oriented computation - a bottom-up problem solving paradigm in which autonomy is the basis of modeling. It aims to model, explain and predict behavior(s) in a complex adaptive system.