Important theoretical aspects of multi-robot coordination mechanisms have, to date, been largely ignored. To address part of this negligence, we focus on the problem of multi-robot task allocation. We give a formal, domainindependent, statement of the problem and show it to be an instance of another, well-studied, optimization problem. In this light, we analyze several recently proposed approaches to multi-robot task allocation, describing their fundamental characteristics in such a way that they can be objectively studied, compared, and evaluated.

In this paper we show how various levels of coordinated behavior may be achieved in a group of mobile robots by using a model of the interaction dynamics between a robot and its environment. We present augmented Markov models (AMMs) as a tool for capturing such interaction dynamics on-line and in real-time, with little computational and storage overhead. We begin by describing the structure of AMMs and the algorithm for generating them, then verify the approach utilizing data from physical mobile robots performing elements of a foraging task. Finally, we demonstrate the application of the model for resolving group coordination issues arising from three sources: individual performance, group affiliation, and group performance. Corresponding respectively to these are the three experimental examples we present --- fault detection, group membership based on ability and experience, and dynamic leader selection. 1 Introduction Learning models of the environment, other robots, and interact...

In this paper, we present an approach to reward maximization in a non-stationary mobile robot environment. The approach works within the constraints of limited local sensing and limited a prior knowledge of the environment. It is based on the use of augmented Markov models (AMMs), which are essentially Markov chains having additional statistics associated with states and state transitions. We have developed an algorithm that constructs AMMs on-line and in real-time with little computational and space overhead, making it practical to maintain multiple models of the interaction dynamics between a robot and its environment during the execution of a task. For the purposes of reward maximization in a non-stationary environment, these models monitor events at increasing intervals of time and provide statistics used to discard redundant or outdated information while reducing the probability of conforming to noise. This approach has been successfully implemented with a real mobile robot perfor...

Introduction Machine learning (ML) [24] is a means of automatically generating solutions that perform better than those that are hand-coded by human programmers. Such improvement is possible in problem domains where optimal solutions are di#cult to identify, i.e., when there are no models available that can accurately relate a system's dynamics to its performance. One such domain is the control of multi-robot systems. Mobile robots are notoriously di#cult to control in a robust, reliable, and repeatable fashion. The challenges stem from uncertainty inherent in physically embodied systems, including in sensors, e#ectors, and interactions between the system components and the environment. The behavior-based (BB) control paradigm [22, 1] provides a means of structuring robot controllers into collections of task-achieving modules or behaviors, such as exploration and obstacle avoidance. The modules operate in parallel and interact within the system and also through their e#ects on the en