Scalability in Self-Organizing Systems: An Experimental Case Study on Foraging Systems

Humann, James; Jin, Yan; Madni, Azad M.

doi:10.1007/978-3-319-62217-0_38

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…The top 5 results, given in Table 6, show a few expected trends. They all use the maximum number of operators (4). They are close to the maximum number of UAVs (8)(9)(10).…”

Section: Scenario 1: Speed Priority With Accuracy Secondarymentioning

confidence: 99%

“…For example, it has been shown that adding semi-autonomous assets to a system only increases system-level performance up to a certain number of agents, and may have no effect or even a detrimental effect if more are added. [4][5][6] Humans' stress, emotional state, and physical condition may all add variability to human-agent team performance, and the workload must be carefully balanced among humans and automated systems. 7 Using human performance simulation can aid systems engineers in balancing workload among the operators, preventing boredom and mistakes, and maximizing the throughput of the overall system.…”

Section: Introductionmentioning

confidence: 99%

“…When human behavior also affects the system, the complex and non‐monotonic effects of human factors 2 and humans' inherent diversity 3 can make the system's behavior even more unpredictable. For example, it has been shown that adding semi‐autonomous assets to a system only increases system‐level performance up to a certain number of agents, and may have no effect or even a detrimental effect if more are added 4–6 . Humans' stress, emotional state, and physical condition may all add variability to human‐agent team performance, and the workload must be carefully balanced among humans and automated systems 7 .…”

Section: Introductionmentioning

confidence: 99%

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Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Humann

Fletcher

Gerdes

2023

Systems Engineering

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As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

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“…The top 5 results, given in Table 6, show a few expected trends. They all use the maximum number of operators (4). They are close to the maximum number of UAVs (8)(9)(10).…”

Section: Scenario 1: Speed Priority With Accuracy Secondarymentioning

confidence: 99%