This work presents a novel approach to efficient multirobot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to being able to withstand communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are given.

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a groundbreaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.

When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job. ABSTRACT | Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to marketbased multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges.

Abstract

As we progress towards a world where robots play an integral role in society, a critical problem that remains to be solved is the Pickup Team challenge; that is, dynamically formed heterogeneous robot teams executing coordinated tasks where little information is known a-priori about the tasks, the robots, and the environments in which they will operate. Successful solutions to forming pickup teams will enable researchers to experiment with larger numbers of robots and enable industry to efficiently and cost-effectively integrate new robot technology with existing legacy teams. In this paper, we define the challenge of pickup teams and propose the treasure hunt domain for evaluating the performance of pickup teams. Additionally, we describe a basic implementation of a pickup team that can search and discover treasure in a previously unknown environment. We build on prior approaches in market-based task allocation and Plays for synchronized task execution, to allocate roles amongst robots in the pickup team, and to execute synchronized team actions to accomplish the treasure hunt task.