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.

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The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots. The work

Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. 1

Abstract—The coordination of a large group of robots to solve a specified task is a difficult problem. Centralized approaches can be computationally intractable, brittle, and unresponsive to change. Distributed approaches are not as prone to these problems, but they can be highly sub-optimal. This work introduces a novel economic approach for coordinating robots based on the free market system. The free market approach defines revenue and cost functions across the possible plans for executing a specified task. The task is accomplished by dividing it into sub-tasks and allowing the robots to bid and negotiate to carry out these sub-tasks. Cooperation and competition emerge as the robots execute the task while trying to maximize their personal profits. Initial simulation results indicate the approach is successful at producing effective global plans for a team of several robots performing an interior sensing task. I.

. While numerous researchers haveinvestigated group behavior of robots which are eachcontrolled in a behavior-based manner, none haveyet thoroughly investigated the possibilities of extending the port-arbitratedbehavior #PAB# paradigm across networks of robots. We present an extension to the well-de#ned PAB techniques of behavioral interaction whichprovides standard abstractions for nessaging, inhibition, and suppression over IP networks. The Broadcast of Local Eligibilityis a general technique built from these abstractions that allows fully-distributed, #exible team coordination. We present a BLE approach to the CMOMMT multi-target observation problem, implemented on a team of physical robots. 1 Introduction While numerous researchers haveinvestigated group behavior of robots which are each controlled in a behavior-based manner, none have yet thoroughly investigated the possibilities of extending the port-arbitrated behavior #12# paradigm across networks of robots. While it has of...

Effective coordination of robots, agents and people promises to improve the safety, robustness and quality with which shared goals are achieved by harnessing the highly heterogeneous entities' diverse capabilities. Proxy-based integration architectures are emerging as a standard method for coordinating teams of heterogeneous entities. Such architectures are designed to meet imposing challenges such as ensuring that the diverse capabilities of the group members are effectively utilized, avoiding miscoordination in a noisy, uncertain environment and reacting flexibly to changes in the environment. However, we contend that previous architectures have gone too far in taking coordination responsibility away from entities and giving it to proxies. Our goal is to create a proxy-based integration infrastructure where there is a beneficial symbiotic relationship between the proxies and the team members. By leveraging the coordination abilities of both proxies and socially capable team members the quality of the coordination can be improved. We present two key new ideas to achieve this goal. First, coordination tasks are represented as explicit roles, hence the responsibilities not the actions are specified, thus allowing the team to leverage the coordination skills of the most capable team members. Second, building on the first idea, we have developed a novel role allocation and reallocation algorithm. These ideas have been realized in a prototype software proxy architecture and used to create heterogeneous teams for an urban disaster recovery domain. Using the rescue domain as a testbed, we have experimented with the role allocation algorithm and observed results to support the hypothesis that leveraging the coordination capabilities of people can help the performance of the te...

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.

We introduce a framework, called "physicomimetics," that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper.

An important issue that arises in the automation of many security, surveillance, and reconnaissance tasks is that of observing the movements of targets navigating in a bounded area of interest. A key research issue in these problems is that of sensor placement -- determining where sensors should be located to maintain the targets in view. In complex applications involving limited-range sensors, the use of multiple sensors dynamically moving over time is required. In this paper, we investigate the use of a cooperative team of autonomous sensor-based robots for the observation of multiple moving targets. In other research, analytical techniques have been developed for solving this problem in complex geometrical environments. However, these previous approaches are very computationally expensive - at least exponential in the number of robots -- and cannot be implemented on robots operating in real-time. Thus, this paper reports on our studies of a simpler problem involving uncluttered environments -- those with either no obstacles or with randomly distributed simple convex obstacles. We focus primarily on developing the on-line distributed control strategies that allow the robot team to attempt to minimize the total time in which targets escape observation by some robot team member in the area of interest. This paper first formalizes the problem (which we term CMOMMT for Cooperative Multi-Robot Observation of Multiple Moving Targets) and discusses related work. We then present a distributed heuristic approach (which we call A-CMOMMT) for solving the CMOMMT problem that uses weighted local force vector control. We analyze the effectiveness of the resulting weighted force vector approach by comparing it to three other approaches. We present the results of our experiments in...