Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction
AbstractMulti-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation. View Full-Text
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Roldán, J.J.; Peña-Tapia, E.; Martín-Barrio, A.; Olivares-Méndez, M.A.; Del Cerro, J.; Barrientos, A. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction. Sensors 2017, 17, 1720.
Roldán JJ, Peña-Tapia E, Martín-Barrio A, Olivares-Méndez MA, Del Cerro J, Barrientos A. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction. Sensors. 2017; 17(8):1720.Chicago/Turabian Style
Roldán, Juan J.; Peña-Tapia, Elena; Martín-Barrio, Andrés; Olivares-Méndez, Miguel A.; Del Cerro, Jaime; Barrientos, Antonio. 2017. "Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction." Sensors 17, no. 8: 1720.
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