Abstract — This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation of patrol algorithms. We then present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. I.

Control of robotic networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multi-robot systems, one great new challenge is the development of distributed motion algorithms that guarantee connectivity of the overall network. In this work we discuss the experimental validation of a distributed algorithm that preserves the connectivity of a team of robots. The algorithm requires limited local information and communication between agents to determine the addition or deletion of network links through distributed consensus and market based auctions. Non-trivial simulation and experimental results demonstrate the effectiveness of the algorithm as a means to guarantee connectivity in a team of robots.

We study auction-like algorithms for the distributed allocation of tasks to cooperating agents. To reduce the team cost of sequential single-item auction algorithms, we generalize them to assign more than one additional task during each round, which increases their similarity to combinatorial auction algorithms. We show that, for a given number of additional tasks to be assigned during each round, every agent needs to submit only a constant number of bids per round and the runtime of winner determination is linear in the number of agents. The communication and winner determination costs do not depend on the number of tasks and thus scale to a large number of tasks for small bundle sizes. We then demonstrate empirically that the team cost of sequential bundlebid single-sale ( = single-item) auction algorithms can be substantially smaller than that without bundles for multiagent routing problems with capacity constraints. 1

Robotic soccer is an adversarial multi-agent research domain, in which issues of perception, multiagent coordination and team strategy are explored. One area of interest investigates heterogeneous teams of humans and robots, where the teammates must coordinate not as master and slave, but as equal participants. We research this peer-to-peer question within the domain of Segway soccer, where teams of humans riding Segway HTs and robotic Segway RMPs coordinate together in competition against other human-robot teams. Beyond the task of physically enabling these robots to play soccer, a key issue in the development of such a heterogeneous team is determining the balance between human and robot player. The first ever Segway soccer competition occurred at the 2005 RoboCup US Open, where demonstrations where held between Carnegie Mellon University (CMU) and the Neurosciences Institute (NSI). Through the execution of these soccer demonstrations, many of the challenges associated with maintaining equality within a peer-to-peer game were revealed. This paper chronicles our experience within the Segway soccer demonstrations at the 2005 US Open, and imparts our interpretation and analysis regarding what is needed to better attain this goal of teammate equality within the peer-to-peer research domain. We begin with an explanation of the motivations behind the Segway soccer and peer-to-peer research, providing details of the

Abstract — This work presents the evaluation of two mission specification and task allocation architectures. These architectures, described in part 1 of this paper, present novel means with which to integrate a case-based reasoning (CBR) mission planner with contract net protocol (CNP) based task allocation. In the first design, the CBR and runtime-CNP architecture, the case-based mission planner generates mission plans that support necessary behaviors for CNP-based task allocation and execution. In the second design, the CBR and premission-CNP architecture, task allocation takes place during mission specification. The results of an empirical evaluation of the CBR and runtime-CNP across three naval scenarios is described. Finally, we briefly describe an earlier usability evaluation of the CBR and premission-CNP architecture using goals, operators, methods, and selection rules modeling. I.

In this paper we compare the costs and benefits of market-based and thresholdbased approaches to task allocation in real world conditions, where information and communication may be limited or inaccurate. We have performed extensive comparative experiments in an event-handling domain. Our results indicate that when information is accurate, market-based approaches are more efficient; when it is not, threshold-based approaches offer the same quality of allocation at a fraction of the expense. Additionally, both approaches are robust to low communication and task perception ranges in our experimental domain. 1

Many coordination algorithms claim to be general, implying that they can be used to coordinate agents in a variety of domains. However, little work has been done to quantitatively compare distinctly different approaches to coordination across a range of domains. In this paper, we present a detailed comparison of two published coordination algorithms, performed in an abstract coordination simulation environment that allows extensive, quantitative experimentation. The simulator is to used to compare two distinct approaches to coordination, token-based coordination and market based coordination. The results largely show the generality of different approaches, but performance and performance tradeoffs varies greatly across domains. 1.

Abstract. In multi-agent planning problems agents are requested to jointly solve a complex task consisting of a set of interrelated tasks. Since none of the agents is capable to solve the whole task on its own, usually each of them is assigned to a subset of tasks. If agents are dependent upon each other via interrelated tasks they are assigned to, moderately-coupled teams of agents are called for. Such teams solve the task by coordinating during or after planning and revising their plans if necessary. In this paper we show that such complex tasks also can be solved by looselycoupled teams of agents that are able to plan independently, although the computational complexity of the coordination problems involved is high. We also investigate some of the factors influencing this complexity. Key words: Multi-agent system, complex tasks, task assignment, planning, coordination, computational complexity.

Abstract. This paper investigates the problem of user interface design and evaluation for autonomous teams of heterogeneous mobile robots. We explore an operator modeling approach to multi-robot user interface evaluation. Specifically the authors generated GOMS models, a type of user model, to investigate potential interface problems and to guide the interface development process. Results indicate that our interface design changes improve the usability of multi-robot mission generation substantially. We conclude that modeling techniques such as GOMS can play an important role in robotic interface development. Moreover, this research indicates that these techniques can be performed in an inexpensive and timely manner, potentially reducing the need for costly and demanding usability studies.

Unmanned aircraft systems (UASs) are now becoming technologically mature enough to be integrated into civil society. An essential issue is principled mixed-initiative interaction between UASs and human operators. Two central problems are to specify the structure and requirements of complex tasks and to assign platforms to them so that they can be achieved. We have previously proposed task specification trees (TSTs) as a highly expressive specification language for complex multiagent tasks that supports mixed-initiative delegation with adjustable autonomy. The main contribution of this paper is a sound and complete distributed heuristic search algorithm for allocating the individual tasks in a TST to platforms. The allocation also instantiates the parameters of the tasks such that all the constraints of the TST are satisfied. Constraints can be used to model dependencies between tasks, resource usage as well as temporal and spatial requirements on the complex task. Finally, we discuss a concrete case study with a team of unmanned aerial vehicles assisting in a challenging emergency services scenario. 1.