The key to utilizing the potential of multirobot systems is cooperation. How can we achieve cooperation in systems composed of failure-prone autonomous robots operating in noisy, dynamic environments? In this paper, we present a novel method of dynamic task allocation for groups of such robots. We implemented and tested an auction-based task allocation system which we call MURDOCH, built upon a principled, resource centric, publish /subscribe communication model. A variant of the Contract Net Protocol, MURDOCH produces a distributed approximation to a global optimum of resource usage. We validated MURDOCH in two very different domains: a tightly coupled multirobot physical manipulation task and a loosely coupled multirobot experiment in long-term autonomy. The primary contribution of this paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.

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. 1

Multirobot coordination, if made efficient and robust, promises high impact on automation. The challenge is to enable robots to work together in an intelligent manner to execute a global task. The market approach has had considerable success in the multirobot coordination domain. This paper investigates the effects of introducing opportunistic optimization with leaders to enhance market-based multirobot coordination. Leaders are able to optimize within subgroups of robots by collecting information about their tasks and status, and re-allocating the tasks within the subgroup in a more profitable manner. The presented work considers the effects of a leader optimizing a single subgroup, and some effects of multiple leaders optimizing overlapping subgroups. The implementations were tested on a variation of the distributed traveling salesman problem. Presented results show that global costs can be reduced, and hence task allocation can be improved, utilizing leaders.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are difficulties such as intractable solutions for large groups, sluggish response to changes in the local environment, heavy communication requirements, and brittle systems with single points of failure. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome the obstacles inherent to centralized approaches, they can only produce suboptimal plans. This work explores the development of a market-based architecture that will be inherently distributed, but will also opportunistically form centralized sub-groups to improve efficiency, and thus

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This paper proposes a distributed algorithm by which a collection of mobile robots roaming on a plane move to form a circle. The algorithm operates under the premises that robots (1) are unable to recall past actions and observations (i.e., oblivious), (2) cannot be distinguished from each others (i.e., anonymous), (3) share no common sense of direction, and (4) are unable to communicate in any other ways than by observing each others position.

Recently auction methods have been investigated as effective, decentralized methods for multi-robot coordination. Experimental research has shown great potential, but has not been complemented yet by theoretical analysis. In this paper we contribute a theoretical analysis of the performance of auction methods for multi-robot routing. We suggest a generic framework for auction-based multi-robot routing and analyze a variety of bidding rules for different team objectives. This is the first time that auction methods are shown to offer theoretical guarantees for such a variety of bidding rules and team objectives.

: While many multi-robot systems rely on fortuitous cooperation between agents, some tasks, such as the assembly of large structures, require tighter coordination. We present a general software architecture for coordinating heterogeneous robots that allows for both autonomy of the individual agents as well as explicit coordination. This paper presents recent results with three robots with very different configurations. Working as a team, these robots are able to perform a high-precision docking task that none could achieve individually. 1. Introduction As robots become more autonomous and sophisticated, they are increasingly being used for more complex and demanding tasks. Often, single robots are insufficient to perform the tasks. For some types of tasks, such as exploration or demining, multiple robots can be used to increase efficiency and reliability. For many other tasks, however, not only are multiple robots necessary, but explicit coordination amongst the robots is imper...

Combinatorial exchanges arise naturally in multi-agent systems that execute hierarchically decomposed tasks, when the agents have uncertainty about each other's tasks and each other's capability of handling tasks. In a combinatorial exchange, the information is aggregated to one party who then decides the task allocation among the agents. Unfortunately, such exchanges can require that bidders calculate and communicate an exponential number of bids, each of which may involve solving a hard planning problem. We present a design for an auctioneer agent that can construct and clear a combinatorial exchange using preference elicitation. This design extends existing analyses of elicitation in the combinatorial auction to the combinatorial exchange. We also introduce the concept of item discovery that uses elicitation to construct the exchange when there is uncertainty about which items should be considered in the market. Our experimental results, in a multi-robot exploration domain, show that elicitation significantly reduces the number of bids that must be evaluated in order to clear the market. More important, the proportion of bids that must be evaluated decreases as we scale to larger problem instances. We also present experimental results for an anytime version of the elicitation algorithm.

This paper reports details of a versatile implementation of the TraderBots approach: A market-based approach to multirobot coordination. The architectural layout, implementation details, and variety of features are described. Experimental results are presented using a team of Pioneer II DX robots engaged in exploration and distributed sensing tasks. Different features and strengths of the approach and the implementation are highlighted in the experimental results.