Incorporating Human Factor Considerations in Unmanned Aerial Vehicle Routing

Murray, Chase; Park, W.

doi:10.1109/tsmca.2012.2216871

Cited by 36 publications

(32 citation statements)

References 45 publications

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“…In military, UAVs with no on-board pilot are remotely controlled by a ground crew and may require significant human interaction [8,19]. Cumming et al [13] and Mehta et al [17] showed improvement in system performance with human-in-the-loop for UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…Ganapathy et al [16] evaluated human-integrated approach in the context of UAV route planning and found that more high priority-targets were covered when humans were involved compared to automation. Additionally, humanmachine interaction is necessary for performance of key activities such as "attack authorization" and "weapon release" [19].…”

Section: Introductionmentioning

confidence: 99%

“…Adding to the complexity of the problem, the UAVs have differing capacities, configurations, and operating characteristics. Similarly, operators-based on their training, experience, and physio-psychological state-have varying efficiencies to perform assigned task [19]. The presented research considers varying operator efficiencies in the complex human-machine interaction system with the objective to maximize effectiveness of the overall system.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, current military systems require a ground crew of three to control a UAV [19]. However, military envisions a future where multiple UAVs are controlled by a single human operator.…”

Section: Introductionmentioning

confidence: 99%

“…Given the military's future vision, concern is that it could result in serious workload problems [9][10][11]. The crew of three operators controlling a single UAV is already experiencing heavy workloads [14,19]. Work overload due to controlling multiple UAVs by a single operator may compromise performance [19,25], increase reliance on automated system [22], result in loss of skills to perform manually when automation fails, and escalate probability of mishaps [21,25,26], collateral damage, and innocent civilians getting killed.…”

Section: Introductionmentioning

confidence: 99%

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Optimal task allocation in multi-human multi-robot interaction

Malvankar-Mehta

Mehta

2015

Optim Lett

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Multi-human multi-robot interaction is a complex system in which robots, e.g., unmanned aerial vehicles, may share information with a group of human operators to perform geographically-dispersed priority-based tasks within a specified time. In this complex system, the key is to optimally allocate tasks comprising of high-risk and low-risk information at multiple-levels in order to maximize effectiveness of the entire system given the limited resources. A multi-level programming model is developed in which an agent allocates information received from multiple robots to multiple team leaders who in turn distribute information to operators within their teams. The objective of the agent is to optimally allocate tasks to multiple team leaders to maximize the overall system performance and to minimize the processing cost and time while considering human factors. The developed model is solved using backward induction and details are presented in reverse time sequence. If human factors are included along with the productivity metrics then the performance of the multi-human multi-robot interaction systems can be improved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal task allocation in multi-human multi-robot interaction

Malvankar-Mehta

Mehta

2015

Optim Lett

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Reliability optimization model in man‐machine systems considering human factors in uncertain situations

Saidi‐Mehrabad,

Atashfeshan,

Razavi

2023

Quality & Reliability Eng

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Over the last four decades, researchers have recognized that traditional automation has many negative consequences stemming from human out‐of‐the‐loop problems. However, there is a paucity of research to optimize function allocation problems under uncertainty by considering both human and machine influential factors. This work attempts to address this standing paucity of research by designing an optimization model with the objective function of maximizing system reliability besides balancing human workload over a mission time and keeping him/her in the control loop. To maximize system reliability, the prediction of human and machine fault rates is inspired by the human‐in‐the‐loop fault tree analysis (FTA). Furthermore, the failure probabilities of human‐related events in the proposed fault tree are estimated via fuzzy logic inference systems. A typical supervisory control task is selected to demonstrate the application and feasibility of the proposed method. In conclusion, this mathematical model as a decision support method provides guidance for automation designers to improve an automated system via systematic prediction of failures in the early stage of the design phase.

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