Designing Human System Interfaces for Supervising Multiple UAV Teams

Hanson, Mark L.; Roth, Emilie M.; Hopkins, Chris; Mancuso, Vince

doi:10.2514/6.2004-1305

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Interactions with planners, therefore, may involve a test loop in which the operator proposes changes and then views test results before implementation (“what-if” testing). Roth, Hanson, Hopkins, Mancuso, and Zacharias (2004) and Hanson, Roth, Hopkins, and Mancuso (2004), for example, report participants’ difficulties in commanding multiple-UAV teams using changes to “intent matrices” that altered relative target values in their planner’s objective function. Their operators expressed confusion over the ordering and choices of targets in the resulting plans and had difficulties in adjusting entries to achieve their desired result.…”

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

“…These results can be contrasted with the ease with which Parasuraman et al’s (2003) participants adapted to using plays in the previous section while working on the same types of problems for the DARPA MICA program. Although the Boeing Experiment Platform (Yang et al, 2004) used in Hanson et al’s (2004) experiments was a more complex, engineering-oriented simulation, the difficulties described appear to arise more from the opacity of the planner than from the complexity of the task.…”

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

“…The crucial feature for successful interaction with cooperative planners appears to lie in the relation between the human’s inputs and intentions and their effect on the plan. A less formal 5-participant study (Malasky, Forest, Kahn, & Key, 2005) of the system for which Hanson et al (2004) reported difficulties in control through an intent matrix showed that allowing operators to choose the initial clusters of targets used by the algorithm for resource allocation and path planning substantially improved the “values” of plans. When the relation between the expression of intent, such as searching or avoiding a region of a map, realizes intelligible expression in a returned plan, cooperative planning can be fruitful and improve performance.…”

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

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Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O(n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(>n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O(n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higherlevel commands are directed to a group. Research in O(n) command has centered on roundrobin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O(n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks. IntRoductIon In recent decades, the number of mobile unmanned vehicles (UVs) deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such UVs into a comprehensive system. The domains of potential application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware, which serve as low-cost replacements for otherwise expensive sensing or motion capabilities, promise to further accelerate the trend toward deploying large teams of mobile UVs. This trend, however, poses a challenge for the control of such systems.

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Section: Interacting With Planners: O(1)mentioning

confidence: 99%

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

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Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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“…Making the information-management demands on the UAV operator even worse are the massive amounts of data now available with net-centric warfare doctrine. Plus, as UAV platforms become more autonomous, it is envisioned that a single crew, or one operator, will be tasked with multiple UAVs simultaneously, in contrast to the current manning of one or more operators to control a single UAV 3 .…”

Section: Introductionmentioning

confidence: 99%

Simulation assessment of synthetic vision system concepts for UAV operations

et al. 2006

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The Air Force Research Laboratory's Human Effectiveness Directorate supports research addressing human factors associated with Unmanned Aerial Vehicle (UAV) operator control stations. One research thrust explores the value of combining synthetic vision data with live camera video presented on a UAV control station display. Information is constructed from databases (e.g., terrain, etc.), as well as numerous information updates via networked communication with other sources. This information is overlaid conformal, in real time, onto the dynamic camera video image display presented to operators. Synthetic vision overlay technology is expected to improve operator situation awareness by highlighting elements of interest within the video image. Secondly, it can assist the operator in maintaining situation awareness of an environment if the video datalink is temporarily degraded. Synthetic vision overlays can also serve to facilitate intuitive communications of spatial information between geographically separated users. This paper discusses results from a high-fidelity UAV simulation evaluation of synthetic symbology overlaid on a (simulated) live camera display. Specifically, the effects of different telemetry data update rates for synthetic visual data were examined for a representative sensor operator task. Participants controlled the zoom and orientation of the camera to find and designate targets. The results from both performance and subjective data demonstrated the potential benefit of an overlay of synthetic symbology for improving situation awareness, reducing workload, and decreasing time required to designate points of interest. Implications of symbology update rate are discussed, as well as other human factors issues. BACKGROUNDThe success of unmanned aerial vehicles (UAVs) in recent military operations has led to increased interest in their capabilities and their application to a variety of missions. Because there is no onboard crew, UAVs offer a number of distinct advantages to traditional, manned aircraft, especially for dull, dirty, and dangerous missions 1 . However, the physical separation of the crew from the aircraft also presents additional challenges to the effective design of the UAV control station. Numerous human factors issues such as system time delays, poor crew coordination, high workload, and reduced situation awareness may negatively affect mission performance 2 . When onboard an aircraft, a pilot and crew instantaneously receive a rich supply of multi-sensory information regarding their surrounding environment. UAV operators, however, may be limited to a time-delayed, reduced stream of sensory feedback delivered almost exclusively through the visual channel. Making the information-management demands on the UAV operator even worse are the massive amounts of data now available with net-centric warfare doctrine. Plus, as UAV platforms become more autonomous, it is envisioned that a single crew, or one operator, will be tasked with multiple UAVs simultaneously, in contrast to the current mann...

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“…Other displays included a synchronization matrix view that allowed the human operator to review the temporal relationships among the sequence of activities planned for each UAV; and resource views that enabled the human operator to view the 'blue' assets available and how the automated controller proposed to allocate them to the different targets that needed to be hit or sensed to accomplish mission goals. See Hanson et al, (2004) for more details on the user interface.…”

Section: Introductionmentioning

confidence: 99%

Human in the Loop Evaluation of a Mixed-Initiative System for Planning and Control of Multiple UAV Teams

Roth¹,

Hanson

Hopkins

et al. 2004

Proceedings of the Human Factors and Ergonomics Society Annual

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This paper describes a ‘Human in the Loop' evaluation of an early prototype mixed-initiative control system that generated plans for single operator supervision of multiple unmanned aerial vehicles (UAV) missions. The objectives of the evaluation were to assess to what extent human operators were able to understand the plans generated by the automated controllers as well as to point to additional support requirements to drive further development of mixed-initiative planning systems. Six former fighter pilots served as test participants. Multiple convergent measures were utilized to evaluate the ability of users to understand and evaluate the plans generated by the automated controller. While test participants were able to understand the plans, the results provided compelling evidence for the need to communicate more effectively the rationale behind plan elements proposed by the automated controller and to provide ‘levers' to enable the user to modify the plan. These results point to important challenges for design of ‘mixed-initiative’ controllers to enable the human and automated controller to function as effective ‘partners’.

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Designing Human System Interfaces for Supervising Multiple UAV Teams

Cited by 4 publications

References 4 publications

Human Interaction With Multiple Remote Robots

Human Interaction With Multiple Remote Robots

Simulation assessment of synthetic vision system concepts for UAV operations

Human in the Loop Evaluation of a Mixed-Initiative System for Planning and Control of Multiple UAV Teams

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