Human-Agent Teaming for Multi-Robot Control: A Literature Review

Jessie, Y. C.; Barnes, Michael

doi:10.21236/ada583900

Cited by 14 publications

(7 citation statements)

References 151 publications

(204 reference statements)

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“…Lyons (2013) identified displays/interfaces to be one of the two means to promote system transparency (i.e., shared awareness and shared intent between the user and automation). There is now emerging research on how to build in transparency (via interfaces) for highly autonomous safety systems (such as Auto-GCAS) in a way that supports operator appropriate trust development (Chen & Barnes, 2014; Lyons, Sadler, et al, 2016; Sadler et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

A Longitudinal Field Study of Auto-GCAS Acceptance and Trust: First-Year Results and Implications

Ho¹,

Sadler²,

Hoffmann³

et al. 2017

Journal of Cognitive Engineering and Decision Making

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In this paper we describe results from the first year of field study examining U.S. Air Force (USAF) F-16 pilots' trust of the Automatic Ground Collision Avoidance System (Auto-GCAS). Using semistructured interviews focusing on opinion development and evolution, system transparency and understanding, the pilotvehicle interface, stories and reputation, usability, and the impact on behavior, we identified factors positively and negatively influencing trust with data analysis methods based in grounded theory. Overall, Auto-GCAS is an effective life-/aircraft-saving technology and is generally well received and trusted appropriately, with trust evolving based on factors including having a healthy skepticism of the system, attributing system faults to hardware problems, and having trust informed by reliable performance (e.g., lives saved). Unanticipated findings included pilots reporting reputation to not be negatively affected by system activations and an interface anticipation cue having the potential to change operational flight behavior. We discuss emergent research avenues in areas of transparency and culture, and values of conducting trust research with operators of realworld systems having high levels of autonomy.

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Section: Discussionmentioning

confidence: 99%

A Longitudinal Field Study of Auto-GCAS Acceptance and Trust: First-Year Results and Implications

Ho¹,

Sadler²,

Hoffmann³

et al. 2017

Journal of Cognitive Engineering and Decision Making

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“…Collaboration with a swarm, depending on mission type, may be considered a secondary task for a human operator. Task switching, which may be hindered more in the presence of interruptions, is prone to lower situation awareness, longer response time, more errors, and lower performance (J. Y. C. Chen & Barnes 2014).…”

Section: Swarm Transparency Challengesmentioning

confidence: 99%

Transparency: Transitioning From Human–Machine Systems to Human-Swarm Systems

Roundtree

Goodrich

Adams

2019

Journal of Cognitive Engineering and Decision Making

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Swarm robotic systems are gaining in interest with the prospect of their use for various applications, including monitoring, tracking, infrastructure support, and protection. Prior human-swarm system research investigated transparency for these systems, but assumed perfect communication scenarios. Real-world human-swarm systems will not have perfect communication due to human processing limitations caused by a large number of individual entities, emergent behavior due to varying environments, and bandwidth issues. Factors that affect transparency or are influenced by transparency were identified from various human–machine system domains; however, understanding the relationship between factors can help designers identify what criteria can be implemented to achieve transparency for swarms. Challenges that may arise based on transparency criteria from human–machine systems are examined to identify improvements for spatial swarm systems.

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“…While shared control collaboration models are notably missing for this task, human-robot collaboration has been explored in other areas of surgical robotics and in numerous other applications of robotics. Moustris et al [17] and Chen et al [18] review human-robot collaboration in more detail for surgical and general robotic applications, respectively.…”

Section: A Backgroundmentioning

confidence: 99%

Toward human-robot collaboration in surgery: Performance assessment of human and robotic agents in an inclusion segmentation task

Kaplan

Nichols

Okamura

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Increasing the level of autonomy in robot-assisted surgery has the potential to improve the safety, speed, and applicability of robot-assisted surgical systems. To facilitate the development and incorporation of robot autonomy in clinical settings, human-robot collaboration models have been suggested in which human and robotic agents work together to accomplish a task. In this work, we measure performance of several human-robot collaboration models in two experiments based on the task of segmenting a stiff inclusion in soft tissue, which simulates a tumor. In the inclusion segmentation experiment, twelve participants explored an artificial tissue and identified the inclusion boundary under the collaboration models of (1) teleoperation, (2) supervised control, (3) traded control, and (4) full autonomy. In the boundary identification experiment, we isolate the performance of human and robotic agents in the boundary identification sub-task; participants and a robotic agent independently identified the boundary of four virtually palpated tissues. Results from the inclusion segmentation experiment indicate that human agents complete the task faster; teleoperation had the fastest task times. Results of both experiments indicate that the robotic agent identifies boundaries with higher sensitivity and less variance than human agents. This indicates that task accuracy increases when a robotic agent segments the boundary, while including a human agent can decrease the overall task time.

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Human-Agent Teaming for Multi-Robot Control: A Literature Review

Cited by 14 publications

References 151 publications

A Longitudinal Field Study of Auto-GCAS Acceptance and Trust: First-Year Results and Implications

A Longitudinal Field Study of Auto-GCAS Acceptance and Trust: First-Year Results and Implications

Transparency: Transitioning From Human–Machine Systems to Human-Swarm Systems

Toward human-robot collaboration in surgery: Performance assessment of human and robotic agents in an inclusion segmentation task

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