Camera guided teleoperation has long been the preferred mode for controlling remote robots, with other modes such as asynchronous control only used when unavoidable. In this experiment we evaluate the usefulness of asynchronous operation for a multirobot search task. Because controlling multiple robots places additional demands on the operator, removing the forced pace for reviewing camera video might reduce workload and improve performance. In the reported experiment participants operated four robot teams performing a simulated urban search and rescue (USAR) task using either conventional streaming video plus a map interface or an experimental interface without streaming video but with the ability to store panoramic images on the map to be viewed at leisure. Search performance was somewhat better using the conventional interface, however, ancillary measures suggest that the asynchronous interface succeeded in reducing temporal demands for switching between robots.

Human control of multiple robots has been characterized by the average demand of single robots on human attention. While this matches situations in which independent robots are controlled sequentially it does not capture aspects of demand associated with coordinating dependent actions among robots. This paper presents an extension of Crandall's neglect tolerance model intended to accommodate both coordination demands (CD) and heterogeneity among robots. The reported experiment attempts to manipulate coordination demand by varying the proximity needed to perform a joint task in two conditions and by automating coordination within subteams in a third. Team performance and the process measure CD were assessed for each condition. Automating cooperation reduced CD and improved performance. We discuss the utility of process measures such as CD to analyze and improve control performance.

The present study investigates the effect of the number of controlled robots on performance of an urban search and rescue (USAR) task using a realistic simulation. Task performance increased in going from four to eight controlled robots but deteriorated in moving from eight to twelve. Workload increased monotonically with number of robots. Performance per robot decreased with increases in team size. Results are consistent with earlier studies suggesting a limit of between 8-12 robots for direct human control. This study demonstrates that these findings generalize to a more realistic setting and complex task.

Human control of multiple robots has been characterized by the average demand of single robots on human attention or the distribution of demands from multiple robots. When robots are allowed to cooperate autonomously, however, demands on the operator should be reduced by the amount previously required to coordinate their actions. The present experiment compares control of small robot teams in which cooperating robots explored autonomously, were controlled independently by an operator or through mixed initiative as a cooperating team. Mixed initiative teams found more victims and searched wider areas than either fully autonomous or manually controlled teams. Operators who switched attention between robots more frequently were found to perform better in both manual and mixed initiative conditions.

This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.

Conventional models of multirobot control assume independent robots and tasks. This allows an additive model in which the operator controls robots sequentially neglecting each until its performance deteriorates sufficiently to require new operator input. This paper presents a model and experiment intended to extend the neglect tolerance model to situations in which robots must cooperate to perform dependent tasks. In the experiment operators controlled 2 robot teams to perform a box pushing task under high cooperation demand (teleoperation), moderate demand (waypoint control/heterogeneous robots), and low demand (waypoint control/homogeneous robots) conditions. Measured demand and performance were consistent with the model’s predictions.

Attitude control refers to controlling the pitch and roll of a mobile robot. As environments become more complex and cues to a robot's pose become sparser, it becomes easy for a teleoperator using an egocentric (camera) display to lose situational awareness. Reported difficulties with teleoperated robots frequently involve rollovers and sometimes even failure to realize that a robot has rolled over. Attitude information has conventionally been displayed separately from the camera view using an artificial horizon graphic or individual indicators for pitch and roll. Information from separate attitude displays may be difficult to integrate with an ongoing navigation task and may lead to errors. In this paper, we report an experiment in which a simulated robot is maneuvered over rough exterior and interior terrains to compare a gravity-referenced view with a separated attitude indication. Results show shorter task times and better path choices for users of the gravity-referenced view but no reduction in rollovers

Attitude control refers to controlling the pitch and roll of a mobile robot. As environments grow more complex and cues to a robot's pose sparser it becomes easy for a teleoperator to lose situational awareness. Information from separated attitude displays may be difficult to integrate with an ongoing navigation task and lead to errors. In this paper we report an experiment comparing a gravity referenced display (GRV) with a standard fixed camera with separated attitude Indication. Results show shorter task times and better path choices for users of the GRV.

We are developing interactive simulations of the National Institute of Standards and Technology (NIST) Reference Test Facility for Autonomous Mobile Robots (Urban Search and Rescue). The NIST USAR Test Facility is a standardized disaster environment consisting of three scenarios of progressive difficulty: Yellow, Orange, and Red arenas. The USAR task focuses on robot behaviors, and physical interaction with standardized but disorderly rubble filled environments. The simulation will be used to test and evaluate designs for teleoperation interfaces and robot sensing and cooperation that will subsequently be incorporated into experimental robots. This paper describes our novel simulation approach using an inexpensive game engine to rapidly construct a visually and dynamically accurate simulation for both individual robots and robot teams.