Tools and Techniques for MOMU (Multiple Operator Multiple UAV) Environments; an Operational Perspective

Oron-Gilad, Tal; Porat, Talya; Fern, Lisa; Draper, Mark H.; Shively, R. Jay; Silbiger, Jacob; Rottem-Hovev, Michal

doi:10.1037/e578902012-018

Cited by 3 publications

(3 citation statements)

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“…Porat, Oron-Gilad, Silbiger, and Rottem-Hovev (2010) propose a multi-UAV viewing technique called “ray castling” to aid operators in choosing which UAV feed to view in order to continue tracking a target that has been obstructed in a currently attended view. Additional techniques for matching size and resolution of video when switching between UAV views are reported in Oron-Gilad et al (2011). Humphrey, Gordon, and Adams (2006) investigated techniques for aiding operators to visualize and control UGVs moving in formation, finding that an idealized semitransparent outline of the formation made it easier for operators to control than either a solid figure obscuring the UGVs or UGV icons without a formation referent.…”

Section: Multi-uv Display and Visualizationmentioning

confidence: 99%

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: Multi-uv Display and Visualizationmentioning

confidence: 99%

Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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“…Fly‐out, fly‐in transitions between different cameras onboard the UAVs have been implemented in Refs. and . Further experiments carried out with a custom‐built human‐swarm interface confirm that an exocentric point of view allows to generally achieve better performances with respect to first ‐person cameras .…”

Section: Human‐swarm Interactionmentioning

confidence: 97%

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

Recchiuto

Sgorbissa

2017

Journal of Field Robotics

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In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post-disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human-swarm interfaces. All these aspects have been analyzed with respect to the state-of-the-art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real-time intervention

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“…In a similar way to Study 2's Twin UAV setup “Payload coupling” enabled the operator to enslave one UAV to the other. Finally, “Camera guide” enabled the operator to fly the UAV by following its camera (See Oron-Gilad et al, 2011 for detailed description of several tools).…”

Section: Studiesmentioning

confidence: 99%

Supervising and Controlling Unmanned Systems: A Multi-Phase Study with Subject Matter Experts

et al. 2016

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Proliferation in the use of Unmanned Aerial Systems (UASs) in civil and military operations has presented a multitude of human factors challenges; from how to bridge the gap between demand and availability of trained operators, to how to organize and present data in meaningful ways. Utilizing the Design Research Methodology (DRM), a series of closely related studies with subject matter experts (SMEs) demonstrate how the focus of research gradually shifted from “how many systems can a single operator control” to “how to distribute missions among operators and systems in an efficient way”. The first set of studies aimed to explore the modal number, i.e., how many systems can a single operator supervise and control. It was found that an experienced operator can supervise up to 15 UASs efficiently using moderate levels of automation, and control (mission and payload management) up to three systems. Once this limit was reached, a single operator's performance was compared to a team controlling the same number of systems. In general, teams led to better performances. Hence, shifting design efforts toward developing tools that support teamwork environments of multiple operators with multiple UASs (MOMU). In MOMU settings, when the tasks are similar or when areas of interest overlap, one operator seems to have an advantage over a team who needs to collaborate and coordinate. However, in all other cases, a team was advantageous over a single operator. Other findings and implications, as well as future directions for research are discussed.

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Tools and Techniques for MOMU (Multiple Operator Multiple UAV) Environments; an Operational Perspective

Cited by 3 publications

References 0 publications

Human Interaction With Multiple Remote Robots

Human Interaction With Multiple Remote Robots

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

Supervising and Controlling Unmanned Systems: A Multi-Phase Study with Subject Matter Experts

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