Remotely operated vehicles (ROVs) are vehicular robotic systems that are teleoperated by a geographically separated user. Advances in computing technology have enabled ROV operators to manage multiple ROVs by means of supervisory control techniques. The challenge of incorporating telepresence in any one vehicle is replaced by the need to keep the human “in the loop” of the activities of all vehicles. An evaluation was conducted to compare the effects of automation level and decision-aid fidelity on the number of simulated remotely operated vehicles that could be successfully controlled by a single operator during a target acquisition task. The specific ROVs instantiated for the study were unmanned air vehicles (UAVs). Levels of automation (LOAs) included manual control, management-by-consent, and management-by-exception. Levels of decision-aid fidelity (100% correct and 95% correct) were achieved by intentionally injecting error into the decision-aiding capabilities of the simulation. Additionally, the number of UAVs to be controlled varied (one, two, and four vehicles). Twelve participants acted as UAV operators. A mixed-subject design was utilized (with decision-aid fidelity as the between-subjects factor), and participants were not informed of decision-aid fidelity prior to data collection. Dependent variables included mission efficiency, percentage correct detection of incorrect decision aids, workload and situation awareness ratings, and trust in automation ratings. Results indicate that an automation level incorporating management-by-consent had some clear performance advantages over the more autonomous (management-by-exception) and less autonomous (manual control) levels of automation. However, automation level interacted with the other factors for subjective measures of workload, situation awareness, and trust. Additionally, although a 3D perspective view of the mission scene was always available, it was used only during low-workload periods and did not appear to improve the operator's sense of presence. The implications for ROV interface design are discussed, and future research directions are proposed.
Supervisory control of multiple unmanned aerial vehicles (UAVs) raises many questions concerning the balance of system autonomy with human interaction for effective operator situation awareness and system performance. The reported experiment used a UAV simulation environment to evaluate two applications of autonomy levels across two primary control tasks: allocation (assignment of sensor tasks to vehicles) and router (determining vehicles’ flight plans). In one application, the autonomy level was the same across these two tasks. In the other, the autonomy levels differed, one of the two tasks being more automated than the other. Trials also involved completion of other mission-related secondary tasks as participants supervised three UAVs. The results showed that performance on both the primary tasks and many secondary tasks was better when the level of automation was the same across the two sequential primary tasks. These findings suggest that having the level of automation similar across closely coupled tasks reduces mode awareness problems, which can negate the intended benefits of a fine-grained application of automation. Several research issues are identified to further explore the impact of automation-level transference in supervisory control applications involving the application of automation across numerous tasks.
Supervisory control of multiple autonomous vehicles raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. An unmanned vehicle simulation designed to manipulate the application of automation was used to evaluate participants' performance on image analysis tasks under two automation control schemes: adaptable (level of automation directly manipulated by participant throughout trials) and adaptive (level of automation adapted as a function of participants' performance on four types of tasks). The results showed that while adaptable automation increased workload, it also improved change detection, as well as operator confidence in task-related decision-making.
This simulation study investigated factors influencing sustained performance and fatigue during operation of multiple Unmanned Aerial Systems (UAS). The study tested effects of time-on-task and automation reliability on accuracy in surveillance tasks and dependence on automation. It also investigated the role of trait and state individual difference factors. Background: Warm's resource model of vigilance has been highly influential in human factors, but further tests of its applicability to complex, real-world tasks requiring sustained attention are necessary. Multi-UAS operation differs from standard vigilance paradigms in that the operator must switch attention between multiple subtasks, with support from automation. Method: 131 participants performed surveillance tasks requiring signal discrimination and symbol counting with a multi-UAS simulation configured to impose low cognitive demands, for 2 hr. Automation reliability was manipulated between-groups. Five Factor Model personality traits were measured prior to performance. Subjective states were assessed with the Dundee Stress State Questionnaire. Results: Performance accuracy on the more demanding surveillance task showed a vigilance decrement, especially when automation reliability was low. Dependence on automation on this task declined over time. State but not trait factors predicted performance. High distress was associated with poorer performance in more demanding task conditions. Conclusions: Vigilance decrement may be an operational issue for multi-UAS surveillance missions. Warm's resource theory may require modification to incorporate changes in information processing and task strategy associated with multitasking in low-workload, fatiguing environments. Application: Interface design and operator evaluation for multi-UAS operations should address issues including motivation, stress, and sustaining attention to automation.
To keep pace with increasing applications of Unmanned Aerial Vehicles (UAVs), recruitment of operators will need to be expanded to include groups not traditionally engaged in UAV pilot training. The present study may inform this process as it investigated the relationship between video game experience and gender on performance of imaging and weapon release tasks in a simulated multi-UAV supervisory control station. Each of 101 participants completed a 60 minute experimental trial. Workload and Level of Automation (LOA) were manipulated. Video gaming expertise correlated with performance on a demanding surveillance task component. Video gamers also placed more trust in the automation in demanding conditions and exhibited higher subjective task engagement and lower distress and worry. Results may encourage recruitment of UAV operators from nontraditional populations. Gamers may have a particular aptitude, and with gaming experience controlled, women show no disadvantage relative to men.
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