A Survey and Analysis

Market-based multirobot coordination approaches have received significant attention and gained considerable popularity within the robotics research community in recent years. 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 by providing an introduction to market-based multirobot coordination, a comprehensive review of the state of the art in the field, and a discussion of remaining challenges

Contents

Market-based multirobot coordination approaches have received significant attention and gained considerable popularity within the robotics research community in recent years. 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 by providing an introduction to market-based multirobot coordination, a comprehensive review of the state of the art in the field, and a discussion of remaining challenges

Robust Multirobot Coordination in Dynamic Environments

Robustness is crucial for any robot team, especially when operating in dynamic environments. The physicality of robotic systems and their interactions with the environment make them highly prone to malfunctions of many kinds. Three principal categories in the possible space of robot malfunctions are communication failures, partial failure of robot resources necessary for task execution (or partial robot malfunction), and complete robot failure (or robot death). This paper addresses these three categories and explores means by which the TraderBots approach ensures robustness and promotes graceful degradation in team performance when faced with malfunctions

Market-Driven Multi-Robot Exploration

For many real-world applications, autonomous robots must execute complex tasks in unknown or partially known unstructured environments. This work presents a novel approach to efficient multi-robot 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 communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are also given

Robust Multirobot Coordination in Dynamic Environments

Robustness is crucial for any robot team, especially when operating in dynamic environments. The physicality of robotic systems and their interactions with the environment make them highly prone to malfunctions of many kinds. Three principal categories in the possible space of robot malfunctions are communication failures, partial failure of robot resources necessary for task execution (or partial robot malfunction), and complete robot failure (or robot death). This paper addresses these three categories and explores means by which the TraderBots approach ensures robustness and promotes graceful degradation in team performance when faced with malfunctions

Market-based Approaches for Coordination of Multi-robot Teams at Different Granularities of Interaction

Multi-robot teams can improve safety and increase human productivity for operations in hazardous environments. To be effective, a control scheme is needed to decompose a task, assign subtasks to individual robots, and synchronize execution. We have developed a market model for this control scheme that realizes the best of both centralized and distributed approaches. In the market approach, robots coordinate opportunistically to meet team constraints and to optimize the team solution. In this paper, we illustrate how the market is used to coordinate at the task decomposition, assignment, and execution phases, depending on the requirements of the given application. We present results from simulation and from actual robots for the applications of mapping, area reconnaissance, and perimeter sweeping