Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: nonsocial, where robots become idle upon experienc...

Multiagent schema-based reactive robotic systems are complemented with the addition of a new behavior controlled by a human operator. This enables the whole society to be aaected as a group rather than forcing the operator to control each agent individually. The operator is viewed by the reactive control system as another behavior exerting his/her innuence on the society as a whole. The operato...

although construction has been known as a highly complex application field for autonomous robotic systems, recent advances in this field offer great hope for using robotic capabilities to develop automated construction. today, space research agencies seek to build infrastructures without human intervention, and construction companies look to robots with the potential to improve construction qua...

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influen...

Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into our simulation to characterize the empir...

We are developing a theory for human control of robot teams based on considering how control varies across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and...

This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environments such as in the complex concurrent multi-robot learning domain. The methodology involves minimizing the learning space through the use of behaviors and conditions, and dealing with the credit assignment problem through shaped reinforcement in the form of heterogeneous reinforcement f...

In the context of large-population multi-objective robot foraging, we present a novel ant-inspired trail-following algorithm that is able to adaptively untangle multiple trails. The emergent result is often a set of short, non-intersecting trails that produce good system throughput due a good trade off between the dual goals of minimizing travel distance and spatial interference. Empirical simu...

Many applications of swarm robotics require autonomous navigation in unknown environments. We describe a new collective navigation strategy based on diffusion limited aggregation and bacterial foraging behaviour. Both methods are suitable for typical swarm robots as they require only minimal sensory and control capabilities. We demonstrate the usefulness of the strategy with a swarm that is cap...