The PackBots being used by the U.S. military in Afghanistan and the urban search and rescue (USAR) robots that worked the World Trade Center site are just two recent examples of mobile robots moving from the laboratory to the field. What is significant about these new applications is that they invariably involve some form of human-robot interaction (HRI) rather than the full robot autonomy that has motivated most prior research. Conducting HRI research can be extremely difficult because experimentation with physical robots is expensive and time consuming. Few roboticists have experience or interest in conducting human experimentation while researchers in human factors or human computer interaction often lack experience in programming robots or access to robotic platforms. In this paper we describe a high fidelity open source simulation intended for HRI researchers of varying backgrounds and providing reference tasks and environments to facilitate collaboration and sharing of results. The architecture and capabilities of the game engine-based USARSim simulation are described. Its use for HRI research is illustrated through case studies describing experiments in camera control for remote viewing and integrated display of attitude information.

Effective communication among agents in large teams is crucial because the members share a common goal but only have a partial views of the environment. Information sharing is difficult in a large team because, a team member may have a piece of valuable information but not know who needs the information, since it is infeasible to know what each other agent is doing. Although much related work has been done on efficient delivery of information, most work is based on assumptions which are not suited to large scale multiagent teams. In this paper

Agents in a team should be in agreement. Unfortunately, they may come to disagree due to sensor uncertainty, intermittent communication failures, etc. Once a disagreement occurs the agents should detect and diagnose the disagreement. Current diagnostic techniques do not scale well with the number of agents, as they have high communication and computation complexity. We present novel techniques that enable scalability in three ways. First, we use communications early in the diagnostic process, to stave off unneeded reasoning, which ultimately leads to unneeded communications. Second, we use light-weight (and inaccurate) behavior recognition to focus the diagnostic reasoning on beliefs of agents that might be in conflict. Finally, we propose diagnosing only to a limited number of representative agents (instead of all the agents). We examine these techniques in large-scale teams of situated agents in two domains, and show that combining the techniques produces a diagnostic process which is highly scalable in both communication and computation.

Bush fires cause major damage each year in many areas of the world and the earlier that they can be detected the easier it is to minimize this damage. This paper describes a collective intelligence algorithm that performs localized rather than centralized control of a number of unmanned aerial vehicles (UAV) that can survey complex areas for fires, devoting attention in proportion to the user specified importance of each area. Simulation shows that not only is the algorithm able to perform this action successfully, it is also able to automatically adapt to a simulated malfunction in one of the UAVs.

Synchronous video has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable as in the case of interplanetary robotics. We identify two basic problems for controlling multiple robots using synchronous displays: operator overload and information fusion. Synchronous displays from multiple robots can easily overwhelm an operator who must search video for targets. If targets are plentiful, the operator will likely miss targets that enter and leave unattended views while dealing with others that were noticed. The related fusion problem arises because robots' multiple fields of view may overlap forcing the operator to reconcile different views from different perspectives and form an awareness of the environment by "piecing them together". We have conducted a series of experiments investigating the suitability of asynchronous displays for multi-UV search. Our first experiments involved static panoramas in which operators selected locations at which robots halted and panned their camera to capture a record of what could be seen from that location. A subsequent experiment investigated the hypothesis that the relative performance of the panoramic display would improve as the number of robots was increased causing greater overload and fusion problems. In a subsequent Image Queue system we used automated path planning and also automated the selection of imagery for presentation by choosing a greedy selection of non-overlapping views. A fourth set of experiments used the SUAVE display, an asynchronous variant of the picture-in-picture technique for video from multiple UAVs. The panoramic displays which addressed only the overload problem led to performance similar to synchronous video while the Image Queue and SUAVE displays which addressed fusion as well led to improved performance on a number of measures. In this paper we will review our experiences in designing and testing asynchronous displays and discuss challenges to their use including tracking dynamic targets.

Abstract. This paper describes the software system supporting the Carnegie Mellon/Univ. of Pittsburgh team of simulated search and rescue robots in the Robocup Rescue 2010 Virtual Robots competition. Building on the Machinetta agent software, robot command and control is decomposed into a hierarchy of subtasks managed by independent agents both on the robot and colocated with human operators. By encapsulating all robot and human operator interactions into interfaces to these agents, the system can perform with a high level of robustness and reusability. As in previous years, the entire code base is portable and platform-independent, running entirely in Java. 1

This work examines the concept of swarm intelligence through three examples of complex problems which are solved by a decentralized swarm of simple agents. The protocols employed by these agents are presented, as well as various analytic results for their performance and for the problems in general. The problems examined are the problem of finding patterns within physical graphs (e.g. k-cliques), the dynamic cooperative cleaners problem, and a problem concerning a swarm of UAVs (unmanned air vehicles), hunting an evading target (or targets). In addition, the work contains a discussion regarding open questions and ongoing and future research in this field.

A progress report submitted for continuation towards a PhD

Abstract. This paper describes the software system supporting the Carnegie Mellon/Univ. of Pittsburgh team of simulated search and rescue robots in the Robocup Rescue 2010 Virtual Robots competition. Building on the Machinetta agent software, robot command and control is decomposed into a hierarchy of subtasks managed by independent agents both on the robot and colocated with human operators. By encapsulating all robot and human operator interactions into interfaces to these agents, the system can perform with a high level of robustness and reusability. As in previous years, the entire code base is portable and platform-independent, running entirely in Java. 1

Abstract not found