Teams organization and performance analysis in autonomous human-robot teams

This paper proposes a theory of human control of robot teams based on considering how people coordinate across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and Rescue (USAR) task. We identify three subtasks: perceptual search-visual search for victims, assistance-teleoperation to assist robot, and navigation-path planning and coordination. For the studies reported here, navigation was selected for automation because it involves weak dependencies among robots making it more complex and because it was shown in an earlier experiment to be the most difficult. This paper reports an extended analysis of the two conditions from a larger four condition study. In these two "shared pool" conditions Twenty four simulated robots were controlled by teams of 2 participants. Sixty paid participants (30 teams) were recruited to perform the shared pool tasks in which participants shared control of the 24 UGVs and viewed the same screens. Groups in the manual control condition issued waypoints to navigate their robots. In the autonomy condition robots generated their own waypoints using distributed path planning. We identify three self-organizing team strategies in the shared pool condition: joint control operators share full authority over robots, mixed control in which one operator takes primary control while the other acts as an assistant, and split control in which operators divide the robots with each controlling a sub-team. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots. © 2010 ACM

Asynchronous displays for multi-UV search tasks

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. © 2012 by the American Institute of Aeronautics and Astronautics, Inc

Characterization of Indicators for Adaptive Human-Swarm Teaming

Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.</p

Task Switching and Single vs. Multiple Alarms for Supervisory Control of Multiple Robots

Foraging tasks, such as search and rescue or reconnaissance, in which UVs are either relatively sparse and unlikely to interfere with one another or employ automated path planning, form a broad class of applications in which multiple robots can be controlled sequen-tially in a round-robin fashion. Such human-robot systems can be described as a queuing sys-tem in which the human acts as a server while robots presenting requests for service are the jobs. The possibility of improving system performance through well-known scheduling tech-niques is an immediate consequence. Unfortunately, real human-multirobot systems are more complex often requiring operator monitoring and other ancillary tasks. Improving perfor-mance through scheduling (jobs) under these conditions requires minimizing the effort ex-pended monitoring and directing the operator’s attention to the robot offering the most gain. Two experiments investigating scheduling interventions are described. The first compared a system in which all anomalous robots were alarmed (Open-queue), one in which alarms were presented singly in the order in which they arrived (FIFO) and a Control condition without alarms. The second experiment employed failures of varying difficulty supporting an optimal shortest job first (SJF) policy. SJF, FIFO, and Open-queue conditions were compared. In both experiments performance in directed attention conditions was poorer than predicted. A possi-ble explanation based on effects of volition in task switching is propose

Teamwork in controlling multiple robots

Simultaneously controlling increasing numbers of robots requires multiple operators working together as a team. Helping operators allocate attention among different robots and determining how to construct the human-robot team to promote performance and reduce workload are critical questions that must be answered in these settings. To this end, we investigated the effect of team structure and search guidance on operators' performance, subjective workload, work processes and communication. To investigate team structure in an urban search and rescue setting, we compared a pooled condition, in which team members shared control of 24 robots, with a sector condition, in which each team member control half of all the robots. For search guidance, a notification was given when the operator spent too much time on one robot and either suggested or forced the operator to change to another robot. A total of 48 participants completed the experiment with two persons forming one team. The results demonstrate that automated search guidance neither increased nor decreased performance. However, suggested search guidance decreased average task completion time in Sector teams. Search guidance also influenced operators' teleoperation behaviors. For team structure, pooled teams experienced lower subjective workload than sector teams. Pooled teams communicated more than sector teams, but sector teams teleoperated more than pool teams.United States. Office of Naval ResearchUnited States. Air Force Office of Scientific Researc

HUMAN CONTROL OF COOPERATING ROBOTS

Advances in robotic technologies and artificial intelligence are allowing robots to emerge fromresearch laboratories into our lives. Experiences with field applications show that we haveunderestimated the importance of human-robot interaction (HRI) and that new problems arise inHRI as robotic technologies expand. This thesis classifies HRI along four dimensions - human,robot, task, and world and illustrates that previous HRI classifications can be successfullyinterpreted as either about one of these elements or about the relationship between two or moreof these elements. Current HRI studies of single-operator single-robot (SOSR) control andsingle-operator multiple-robots (SOMR) control are reviewed using this approach.Human control of multiple robots has been suggested as a way to improve effectiveness inrobot control. Unlike previous studies that investigated human interaction either in low-fidelitysimulations or based on simple tasks, this thesis investigates human interaction with cooperatingrobot teams within a realistically complex environment. USARSim, a high-fidelity game-enginebasedrobot simulator, and MrCS, a distributed multirobot control system, were developed forthis purpose. In the pilot experiment, we studied the impact of autonomy level. Mixed initiativecontrol yielded performance superior to fully autonomous and manual control.To avoid limitation to particular application fields, the present thesis focuses on commonHRI evaluations that enable us to analyze HRI effectiveness and guide HRI design independentlyof the robotic system or application domain. We introduce the interaction episode (IEP), whichwas inspired by our pilot human-multirobot control experiment, to extend the Neglect ToleranceHUMAN CONTROL OF COOPERATING ROBOTSJijun Wang, Ph.D.University of Pittsburgh, 2007vmodel to support general multiple robots control for complex tasks. Cooperation Effort (CE),Cooperation Demand (CD), and Team Attention Demand (TAD) are defined to measure thecooperation in SOMR control. Two validation experiments were conducted to validate the CDmeasurement under tight and weak cooperation conditions in a high-fidelity virtual environment.The results show that CD, as a generic HRI metric, is able to account for the various factors thataffect HRI and can be used in HRI evaluation and analysis