A dirty-bomb experiment conducted at the INL is used to evaluate the effectiveness and suitability of three different modes of robot control. The experiment uses three distinct user groups to understand how participants' background and training affect the way in which they use and benefit from autonomy. The results show that the target mode, which involves automated mapping and plume tracing together with a point and click tasking tool, provides the best performance for each group. This is true for objective performance such as source detection and localization accuracy as well as subjective measures such as perceived workload, frustration and preference. The best overall performance is achieved by the Explosive Ordinance Disposal group which has experience in both robot teleoperation and dirty bomb response. The user group that benefits least from autonomy is the Nuclear Engineers that have no experience with either robot operation or dirty bomb response. The group that benefits most from autonomy is the Weapons of Mass Destruction Civil Support Team that has extensive experience related to the task, but no robot training.

In previous work, increased complexity of robot behaviors and the accompanying interface design often led to operator confusion and/or a fight for control between the robot and operator. We believe the reason for the conflict was that the design of the interface and interactions presented too much of the underlying robot design model to the operator. Since the design model includes the implementation of sensors, behaviors, and sophisticated algorithms, the result was that the operator's cognitive efforts were focused on understanding the design of the robot system as opposed to focusing on the task at hand. This paper illustrates how this very problem emerged at the INL and how the implementation of new metaphors for interaction has allowed us to hide the design model from the user and allow the user to focus more on the task at hand. Supporting the user's focus on the task rather than on the design model allows increased use of the system and significant performance improvement in a search task with novice users.

Navigation is an essential element of many remote robot operations including search and rescue, reconnaissance, and space exploration. Previous reports on using remote mobile robots suggest that navigation is difficult due to poor situation awareness. It has been recommended by experts in human-robot interaction that interfaces between humans and robots provide more spatial information and better situational context in order to improve an operator's situation awareness. This paper presents an ecological interface paradigm that combines video, map, and robot-pose information into a 3-D mixed-reality display. The ecological paradigm is validated in planar worlds by comparing it against the standard interface paradigm in a series of simulated and real-world user studies. Based on the experiment results, observations in the literature, and working hypotheses, we present a series of principles for presenting information to an operator of a remote robot.

One of the fundamental aspects of robot teleoperation is the ability to successfully navigate a robot through an environment. We define successful navigation to mean that the robot minimizes collisions and arrives at the destination in a timely manner. Often video and map information is presented to a robot operator to aid in navigation tasks. This paper addresses the usefulness of map and video information in a navigation task by comparing a side-by-side (2D) representation and an integrated (3D) representation in both a simulated and a real world study. The results suggest that sometimes video is more helpful than a map and other times a map is more helpful than video. From a design perspective, an integrated representation seems to help navigation more than placing map and video side-by-side.

This paper presents results from three experiments in which human operators were teamed with a mixed-initiative robot control system to accomplish various indoor search and exploration tasks. By assessing human workload and error together with overall performance, these experiments provide an objective means to contrast different modes of robot autonomy and to evaluate both the usability of the interface and the effectiveness of autonomous robot behavior. The first experiment compares the performance achieved when the robot takes initiative to support human driving with the opposite case when the human takes initiative to support autonomous robot driving. The utility of robot autonomy is shown through achievement of better performance when the robot is in the driver's seat. The second experiment introduces a virtual three-dimensional (3-D) map representation that supports collaborative understanding of the task and environment. When used in place of video, the 3-D map reduced operator workload and navigational error. By lowering bandwidth requirements, use of the virtual 3-D interface enables long-range, nonline-of-sight communication. Results from the third experiment extend the findings of experiment 1 by showing that collaborative control can increase performance and reduce error even when the complexity of the environment is increased and workload is distributed amongst multiple operators.

The ability of robots to autonomously perform tasks is increasing. More autonomy in robots means that the human managing the robot may have available free time. It is desirable to use this free time productively, and a current trend is to use this available free time to manage multiple robots. We present the notion of neglect tolerance as a means for determining how robot autonomy and interface design determine how free time can be used to support multitasking, in general, and multirobot teams, in particular. We use neglect tolerance to 1) identify the maximum number of robots that can be managed; 2) identify feasible configurations of multirobot teams; and 3) predict performance of multirobot teams under certain independence assumptions. We present a measurement methodology, based on a secondary task paradigm, for obtaining neglect tolerance values that allow a human to balance workload with robot performance.

Most interfaces for robot control have focused on providing users with the most current information and giving status messages about what the robot is doing. While this may work for people that are already experienced in robotic, we need an alternative paradigm for enabling new users to control robots effectively. Instead of approaching the problem as an issue of what information could be useful, the focus should be on presenting essential information in an intuitive way. One way to do this is to leverage perceptual cues that people are accustomed to using. By displaying information in such contexts, people are able to understand and use the interface more effectively. This paper presents interfaces which allow users to navigate in 3-D worlds with integrated range and camera information.

In this paper we develop a method for predicting the performance of human-robot teams consisting of a single user and multiple robots. To predict the performance of a team, we first measure the neglect tolerance and interface efficiency of the interaction schemes employed by the team. We then describe a method that shows how these measurements can be used to estimate the team's performance. We validate the performance prediction algorithm by comparing predictions to actual results when a user guides three robots in an exploration and goal-finding mission; comparisons are made for various system configurations.