Managing Cockpit Crew Excess Task Load in Military Manned-Unmanned Teaming Missions by Dual-Mode Cognitive Automation Approaches

Strenzke, Ruben; Uhrmann, Johann; Benzler, Andreas; Maiwald, Felix; Rauschert, Andreas; Schulte, Axel

doi:10.2514/6.2011-6237

Cited by 33 publications

(10 citation statements)

References 20 publications

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“…Communications among agents; difficulty in dealing with truly novel situations (algorithms too cumbersome when dealing with complex hierarchies). Hou et al, 2011;Strenzke et al, 2011 Adaptive Automation…”

Section: Agents For Human-robot Teamingmentioning

confidence: 99%

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

Jessie¹,

Barnes²

2013

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The literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots; appropriate human trust in the automated systems; maintenance of human operator's situation awareness; individual differences in human-agent (H-A) interaction; and retention of human decision authority. A number of approaches, from flexible automation to autonomous agents, were reviewed and their advantages and disadvantages were discussed. Also discussed were two key human performance issues (trust and situation awareness) related to H-A teaming for multi-robot control and some promising user interface design solutions to address these issues. Some key individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.

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Section: Agents For Human-robot Teamingmentioning

confidence: 99%

“…Agent technologies with military import have been demonstrated for a number of realistic applications. For example, agents have been successfully used to control multiple UAVs with received signal indicator sensors to locate targets cooperatively during high fidelity simulations (Scerri et al, 2008 (Strenzke, 2011).…”

Section: Hierarchical Agentsmentioning

confidence: 99%

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

Jessie¹,

Barnes²

2013

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“…Manned-Unmanned Teaming (MUM-T) can be seen as a cooperative teaming of multiple agents: several Unmanned Aerial Vehicles (UAVs) and possibly several manned aircrafts. The MUM-T organization described by Strenzke and collaborators contains multiple UAVs and a human operator present in a manned aircraft along with a flying pilot (Strenzke et al, 2011 ). However, our vision for the future of MUM-T missions is a team of several agents, in which an agent could be an artificial one—i.e., a UAV- or a human.…”

Section: Introductionmentioning

confidence: 99%

“…The implementation of such a MUM-T organization will require more cooperation and coordination between the agents, that could increase the mental workload of the human agent. However, there are immense advantages to this approach as for instance: benefiting from the faster and more calculative capabilities of the artificial agents, and for the human agents' better perception, judgment abilities and critical thinking (de Souza et al, 2020 ), increasing mission achievement chances while ensuring safety (Chanel et al, 2020b ), or enabling a better proximity and state awareness of the human agents (Strenzke et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Mental Workload Estimation Based on Physiological Features for Pilot-UAV Teaming Applications

Singh

Chanel

Roy

2021

Front. Hum. Neurosci.

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Manned-Unmanned Teaming (MUM-T) can be defined as the teaming of aerial robots (artificial agents) along with a human pilot (natural agent), in which the human agent is not an authoritative controller but rather a cooperative team player. To our knowledge, no study has yet evaluated the impact of MUM-T scenarios on operators' mental workload (MW) using a neuroergonomic approach (i.e., using physiological measures), nor provided a MW estimation through classification applied on those measures. Moreover, the impact of the non-stationarity of the physiological signal is seldom taken into account in classification pipelines, particularly regarding the validation design. Therefore this study was designed with two goals: (i) to characterize and estimate MW in a MUM-T setting based on physiological signals; (ii) to assess the impact of the validation procedure on classification accuracy. In this context, a search and rescue (S&R) scenario was developed in which 14 participants played the role of a pilot cooperating with three UAVs (Unmanned Aerial Vehicles). Missions were designed to induce high and low MW levels, which were evaluated using self-reported, behavioral and physiological measures (i.e., cerebral, cardiac, and oculomotor features). Supervised classification pipelines based on various combinations of these physiological features were benchmarked, and two validation procedures were compared (i.e., a traditional one that does not take time into account vs. an ecological one that does). The main results are: (i) a significant impact of MW on all measures, (ii) a higher intra-subject classification accuracy (75%) reached using ECG features alone or in combination with EEG and ET ones with the Adaboost, Linear Discriminant Analysis or the Support Vector Machine classifiers. However this was only true with the traditional validation. There was a significant drop in classification accuracy using the ecological one. Interestingly, inter-subject classification with ecological validation (59.8%) surpassed both intra-subject with ecological and inter-subject with traditional validation. These results highlight the need for further developments to perform MW monitoring in such operational contexts.

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“…Both high-fidelity, human-in-the-loop simulation and real UAV flights were demonstrated. Cognitive agents provided intent-based UAV guidance, planning, and decisionmaking support, and workload-and resource-adaptive interaction in a "cooperative control" arrangement (Schulte & Donath, 2011;Strenzke et al, 2011).…”

Section: Nato Rto Hfm 170/217 and Its Technology Demonstrationsmentioning

confidence: 99%

Frameworks for Supervisory Control: Characterizing Relationships with Uninhabited Vehicles

Miller

2013

Journal of Human-Robot Interaction

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This article reports efforts to devise frameworks for quickly and easily characterizing 13 different supervisory control applications developed and tested by the international participants in a North Atlantic Treaty Organization working group on supervisory control of multiple uninhabited vehicles (UVs). Multiple prior frameworks for supervisory control are reviewed, and two novel ones (a more complex one involving seven descriptive parameters and a simpler one involving only two) are proposed that build on prior work. These frameworks are used to characterize the group's technology demonstrations. The insights gleaned from each analysis are discussed in the context of the needs and technology usage assumptions shared by the group, along with the costbenefit tradeoff of applying such frameworks.

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Managing Cockpit Crew Excess Task Load in Military Manned-Unmanned Teaming Missions by Dual-Mode Cognitive Automation Approaches

Cited by 33 publications

References 20 publications

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

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

Mental Workload Estimation Based on Physiological Features for Pilot-UAV Teaming Applications

Frameworks for Supervisory Control: Characterizing Relationships with Uninhabited Vehicles

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