Using Interference Models to Predict Performance in a Multiple-Task UAV Environment - 2 UAVs

Wickens, Christopher D.; Dixon, Stephen; Chang, Dervon

doi:10.21236/ada496812

Cited by 27 publications

(27 citation statements)

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“…Adherence to multiple principles are required to make this possible, including offloading low-level control of the robots to automation [3], [4], [5], [6], ensuring that the automation is reliable [7], and improving interface technologies (e.g. [8], [9]). Predictive metrics provide a means to evaluate these technologies in a cost-effective manner.…”

Section: A Related Workmentioning

confidence: 99%

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

Crandall

Cummings

2007

IEEE Trans. Robot.

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Abstract-In recent years, much research has focused on making possible single operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes. We assess the ability of this set of metric classes to (a) predict the number of robots that should be in the team and (b) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.

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Section: A Related Workmentioning

confidence: 99%

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

Crandall

Cummings

2007

IEEE Trans. Robot.

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“…LOAs can vary across the embedded control loops, and a general assignment of a single LOA across the three loops makes it difficult to determine how to effectively model and intervene to free specific cognitive resources, either from an automation or decision support perspective. Wickens et al [7,8] demonstrated that automating the guidance and motion control loop reduced operator workload by freeing cognitive resources for other tasks, and that some automation for the navigation and mission management control loops was helpful in reducing operator workload. However, they also did not distinguish between levels of automation for the navigation and mission management loops, thus it is not clear how the levels of automation in each loop affected the other and the overall outcome of the mission.…”

Section: Levels Of Automationmentioning

confidence: 99%

Operator scheduling strategies in supervisory control of multiple UAVs

Cummings

Mitchell

2007

Aerospace Science and Technology

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The application of network centric operations to time-constrained command and control environments will mean that human operators will be increasingly responsible for multiple simultaneous supervisory control tasks. One such futuristic application will be the control of multiple unmanned aerial vehicles (UAVs) by a single operator. To achieve such performance in complex, time critical, and high risk settings, automated systems will be required both to guarantee rapid system response as well as manageable workload for operators. Through the development of a simulation test bed for human supervisory control of multiple independent UAVs by a single operator, this paper presents recent efforts to investigate workload mitigation strategies as a function of increasing automation. A humanin-the-loop experiment revealed that under low workload conditions, operators' cognitive strategies were relatively robust across increasing levels of automated decision support. However, when provided with explicit automated recommendations and with the ability to negotiate with external agencies for delays in arrival times for targets, operators inappropriately fixated on the need to globally optimize their schedules. In addition, without explicit visual representation of uncertainty, operators tended to treated all probabilities uniformly. This study also revealed that operators that reached cognitive saturation adapted two very distinct management strategies, which led to varying degrees of success. Lastly, operators with management-by-exception decision support exhibited evidence of automation bias.Keywords: multiple unmanned aerial vehicles, human supervisory control, levels of automation * Corresponding author, 77Massachusetts Ave, 33-305, Cambridge, MA 02139, missyc@mit.edu, 1-617-252-1512, 1-617-253-4196 (fax). IntroductionHuman supervisory control (HSC) occurs when a human operator monitors a complex system and intermittently executes some level of control on the system though some automated agent. Because HSC is not typically a continuous task, HSC operators can typically time-share cognitive resources with other tasks. This task time-sharing is critical for the success of network-centric operations (NCO) which require operators to process information from a variety of sources, often within a short period of time. One such domain where HSC and NCO will intersect in the future will be in unmanned vehicle operations. While many operators are presently needed to control a single unmanned aerial vehicle (UAV), as technology and autonomous control improve, automation will handle lower level tasks, thus enabling the supervisory controller more time to control a greater number of UAVs. The key to achieving this one-controlling-many goal will be the development of automated control architectures that compliment human decision-making processes in time-critical environments. A significant cognitive control issue for humans managing multiple vehicles is that they must synthesize voluminous data from a network of sensors and veh...

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“…In fact, reaction time almost doubled when the number of displays increased from one to two and from two to three (a slope of 1.94 was obtained). Moreover, according to Wickens, Dixon, and Chang (2003), visual angle separation larger than ϳ6.4°-7.5°may degrade eventmonitoring response time. Additionally, research on visual performance demonstrated that as the size of the search set increased, performance degraded in terms of either speed or accuracy or both (Scanlan, 1977).…”

Section: Introductionmentioning

confidence: 99%

Concurrent Performance of Military and Robotics Tasks and Effects of Cueing in a Simulated Multi-Tasking Environment

Chen

2009

Presence: Teleoperators and Virtual Environments

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We simulated a military mounted crewstation environment and conducted two experiments to examine the workload and performance of the combined position of gunner and robotics operator. The robotics tasks involved managing a semiautonomous ground robot or teleoperating a ground robot to conduct reconnaissance tasks. We also evaluated whether aided target recognition (AiTR) capabilities (delivered either through tactile or tactile ϩ visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Results showed that participants' gunnery task performance degraded significantly when they had to concurrently monitor, manage, or teleoperate an unmanned ground vehicle compared to the gunnery-single task condition. When there was AiTR to assist them with their gunnery task, operators' concurrent performance of robotics and communication tasks improved significantly. However, there was a tendency for participants to over-rely on automation when task load was heavy, and performance degradations were observed in instances where automation failed to be entirely reliable. Participants' spatial ability was found to be a reliable predictor of robotics task performance, although the performance gap between those with higher and lower spatial ability appeared to be narrower when the AiTR was available to assist the gunnery task. Participants' perceived workload increased consistently as the concurrent task conditions became more challenging and when their gunnery task was unassisted. Individual difference factors such as spatial ability and perceived attentional control were found to correlate significantly with some of the performance measures. Implications for military personnel selection were discussed.

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Using Interference Models to Predict Performance in a Multiple-Task UAV Environment - 2 UAVs

Cited by 27 publications

References 84 publications

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

Operator scheduling strategies in supervisory control of multiple UAVs

Concurrent Performance of Military and Robotics Tasks and Effects of Cueing in a Simulated Multi-Tasking Environment

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