Asynchronous Control with ATR for Large Robot Teams

Brooks, Nathan; Scerri, Paul; Sycara, Katia; Wang, Huadong; Chien, Shih‐Yi; Lewis, Michael

doi:10.1037/e578902012-091

Cited by 1 publication

(2 citation statements)

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“…The ImageQueue (Brooks et al, 2011; Wang et al, 2011) accomplishes this by storing UGV pose and laser scans along with video frames allowing the area viewed to be computed. A greedy algorithm then selected the smallest set of images that covered the greatest area, removing areas with viewed imagery from the queue, as shown in Figure 4.9.…”

Section: Multi-uv Display and Visualizationmentioning

confidence: 99%

“…Fielded multirobot control interfaces, such as SAGE (Olson et al, 2012), differ in detail but feature similar map and vehicle-centric displays with selection of individual robots for detailed examination of sensor data or control. Studies using USARSim are reviewed under LOAs (Chien, Mehrotra, Brooks, Sycara, & Lewis, 2011; Valero-Gomez, Puente, & Hernando, 2011), characteristics of O( n ) systems (Lewis et al, 2010; Humphrey, Henk, Sewell, Williams, & Adams, 2007), aiding through scheduling (Gao, Cummings, & Bertuccelli, 2012), 3.2.2 (Wang & Lewis, 2007b), and multi-UV display (Brooks et al, 2011; Wang et al, 2011).…”

Section: Introductionmentioning

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: Multi-uv Display and Visualizationmentioning

confidence: 99%