Introduction The use of robots to explore space has many advantages over using humans for the same task. However, a shortcoming of exploration using robotic entities has been the lack of ability to create sustainable teams with enough flexibility to perform numerous tasks, provide a high-level of versatility, and work as a cooperative team to satisfy mission critical goals. The ability for a robotic colony to create and sustain itself, in the long-term, in a planetary or space environment, will require the flexibility to reorganize and reform itself to meet and overcome continuous, unforeseen challenges. Our research deals with the development of an organizational model and implementation that allows a robotic team to continuously pursue mission goals. While working through tasks to accomplish the goal(s), the team will examine its current operational state and decide whether goal satisfaction can occur by utilizing the current organization or if a more efficient organization, consist

entities that often must be decomposed to have deliverable outputs and used to identify the critical aspects of system requirements.

Abstract — Many application tasks require the cooperation of two or more robots. Humans are good at cooperation in shared workspaces, because they anticipate and adapt to the intentions and actions of others. In contrast, multi-agent and multi-robot systems rely on communication to exchange their intentions. This causes problems in domains where perfect communication is not guaranteed, such as rescue robotics, autonomous vehicles participating in traffic, or robotic soccer. In this paper, we introduce a computational model for implicit coordination, and apply it to a typical coordination task from robotic soccer: regaining ball possession. The computational model specifies that performance prediction models are necessary for coordination, so we learn them off-line from observed experience. By taking the perspective of the team mates, these models are then used to predict utilities of others, and optimize a shared performance model for joint actions. In several experiments conducted with our robotic soccer team, we evaluate the performance of implicit coordination. I.

updated with select publications that appeared between its original publication date in December 2005 and the article’s publication date in July 2006. As robotic technology improves, we charge robots with increasingly varied and difficult tasks. Many of these tasks can potentially be completed better by a team of robots working together than by individual robots working alone. Coordination can lead to faster task completion, increased robustness, higher-quality solutions, and the completion of tasks impossible for single robots. Nevertheless, effective coordination can be difficult to achieve because of a range of adverse real-world conditions including dynamic events, changing task demands, resource failures, and limited deliberation time. The desire to overcome these challenges and harness the benefits of robot teams has made multirobot coordination a vital field in robotics research. Of the resulting wealth of research, market-based multirobot coordination approaches in particular have received significant attention and are growing in popularity within the community. These approaches harness the principles of market economies—which

Abstract: This paper studies market-based mechanisms for dynamic coordinated task assignment in large scale agent systems carrying out search and rescue missions. Specifically, the effect of different auction mechanisms and swapping are studied. The paper describes results from a large number of simulations of homogeneous agents, where by homogeneous we mean that agents in a given simulation use the same strategy. The information available to agents and their bidding strategies are used as simulation parameters. The simulations provide insight about the interaction between the strategy used by individual agents and the market mechanism. Performance is evaluated using several metrics: mission time, distance traveled, communication and computation costs, and workload distribution. Some of the results obtained include: limiting information may improve performance, different utility functions may affect the performance in non-uniform ways, and swapping may help improve the efficiency of assignments in dynamic environments.

Abstract — We have explored principled mechanisms for converting a hierarchical organization to an edge type organization. Other than structural differences, organizations differ in information flow network and information sharing strategies. Beyond current effort, many other types of organizational adaptation are possible and require much further research that we anticipate to remain for future work. This article lays the foundation for automatic organizational adaptation. I.

Summary. Task allocation is a complex and open problem for multi-robot systems and very especially if a priority is associated to each task. In this paper, we present a method to allocate tasks with priorities in a team of heterogeneous robots. The system is partially inspired on auction and thresholds-based methods and tries to determine the optimum number of robots that are needed to solve specific tasks taking into account their priorities and characteristics. Thus, we can minimize the interference effect between robots and increase the system performance. The method has been extensively tested for a modification of the well-known foraging task, using different kinds of robots. Experimental results are presented to show the benefits of the proposed method. 1

Autonomous Robot Colony Using LEGO ® Mindstorms™. (Under the direction of Dr.

This paper presents a methodology for modeling the coordination of multi-robot teams in the execution of cooperative tasks. The main idea is to use hybrid systems theory in order to model the cooperation.

This dissertation is concerned with the problem of intelligently coordinating large-scale synthetic systems, focusing on the domain of sensor-actuator networks. As the fields of robotics and embedded computing have evolved and matured, they have become increasingly similar to each other. Many of the problem domains (and proposed solutions) and much of the enabling infrastructure (e.g., low-power processors, wireless networking) are common to the two fields. As a result, a new area of research is emerging that focuses on a generalization of embedded and robotic systems, known as sensor-actuator networks (SANs). Like a conventional computer network, a SAN is a network of nodes, each of which possesses some computing and communication resources. However, SAN nodes are also attached to sensors and/or actuators that allow them to, respectively, perceive and change their environment. The goal of this dissertation is to achieve efficient, scalable, fault-tolerant cooperative behavior in networks of such nodes applied to complex tasks. In approaching this difficult distributed control problem, I take inspiration from a method for self-organization commonly employed by people: the free market. Specifically, I advocate the use of distributed auctions for the allocation of resources in SANs. Auctions, in one form or another, have been used in societies throughout history to allocate scarce resources among