An Analysis of Heterogeneity in Futuristic Unmanned Vehicle Systems

Recent studies have shown that with appropriate operator decision support and with enough automation aboard unmanned vehicles, inverting the multiple operators to single-vehicle control paradigm is possible. These studies, however, have generally focused on homogeneous teams of vehicles, and have not completely addressed either the manifestation of heterogeneity in vehicle teams, or the effects of heterogeneity on operator capacity. An important implication of heterogeneity in unmanned vehicle teams is an increase in the diversity of possible team configurations available for each operator, as well as an increase in the diversity of possible attention allocation schemes that can be utilized by operators. To this end, this paper introduces a resource allocation framework that defines the strategies and processes that lead to alternate team configurations. The framework also highlights the sub-components of operator attention allocation schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. A subsequent discrete event simulation model of a single operator supervising multiple heterogeneous vehicles and tasks explores operator performance under different heterogeneous team compositions and varying attention allocation strategies. Results from the discrete event simulation model show that the change in performance when switching from a homogeneous team to a heterogeneous one is highly dependent on the change in operator utilization. Heterogeneous teams that result in lower operator utilization can lead to improved performance under certain operator strategies.Prepared for Charles River Analytic

Mitigation of Human Supervisory Control Wait Times through Automation Strategies

The application of network centric operations principles to human supervisory control (HSC) domains means that humans are increasingly being asked to manage multiple simultaneous HSC processes. However, increases in the number of available information sources, volume of information and operational tempo, all which place higher cognitive demands on operators, could become constraints limiting the success of network centric processes. In time-pressured scenarios typical of networked command and control scenarios, efficiently allocating attention between a set of dynamic tasks is crucial for mission success. Inefficient attention allocation leads to system wait times, which could eventually lead to critical events such as missed times on targets and degraded overall mission success. One potential solution to mitigating wait times is the introduction of automated decision support in order to relieve operator workload. However, it is not obvious what automated decision support is appropriate, as higher levels of automation may result in a situation awareness decrement and other problems typically associated with excessive automation such as automation bias. To assess the impact of increasing levels of automation on human and system performance in a time-critical HSC multiple task management context, an experiment was run in which an operator simultaneously managed four highly autonomous unmanned aerial vehicles (UAVs) executing an air tasking order, with the overall goal of destroying a pre-determined set of targets within a limited time period. Four increasing levels automated decision support were investigated as well as high and low operational replanning tempos. The highest level of automation, management-byexception, had the best performance across several metrics but had a greater number of catastrophic events during which a UAV erroneously destroyed a friendly target. Contrary to expectations, the collaborative level of decision support, which provided predictions for possible periods of task overload as well as possible courses of action to relieve the high workload, produced the worst performance. This is attributable to an unintended consequence of the automation where the graphical visualization of the computer’s predictions caused users to try to globally optimize the schedules for all UAVs instead of locally optimizing schedules in the immediate future, resulting in them being overwhelmed. Total system wait time across both experimental factors was dominated by wait time caused by lack of situation awareness, which is difficult to eliminate, implying that there will be a clear upper limit on the number of vehicles that any one person can supervise because of the need to stay cognitively aware of unfolding events.Prepared for Boeing, Phantom Work

The Effect of Level of Automation and Operator-to-Vehicle Ratio on Operator Workload and Performance in Future UAV Systems

The military intends to increase the number of UAVs in service while at the same time reducing the number of operators (Dixon, Wickens & Chang; 2004). To meet this demand, many of the current UAV operator functions will need to be automated. How automation is applied to modern systems is not fixed. Levels of automation exist along a continuum from fully manual to fully automatic. Two proposed levels of automation for future UAV systems are Management by Consent (MBC), where the operator selects the task to be executed, and Management by Exception (MBE), where the computer selects the task to be executed are. The optimum operator-to-vehicle ratio for future UAV systems is not yet known. It is expected that the optimum operator-to-vehicle ratio will vary with the level of automation applied to the system. Future systems may require the use of adaptive automation to ensure maximum human-machine performance across varying operator-to-vehicle ratios. This study aims to help determine what levels of automation are most appropriate for different operator-to-vehicle ratios and how adaptive automation should be applied in future UAV systems

Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control

Operators currently controlling Unmanned Aerial Vehicles report significant boredom, and such systems will likely become more automated in the future. Similar problems are found in process control, commercial aviation, and medical settings. To examine the effect of boredom in such settings, a long duration low task load experiment was conducted. Three low task load levels requiring operator input every 10, 20, or 30 minutes were tested in a our-hour study using a multiple unmanned vehicle simulation environment that leverages decentralized algorithms for sometimes imperfect vehicle scheduling. Reaction times to system-generated events generally decreased across the four hours, as did participants’ ability to maintain directed attention. Overall, participants spent almost half of the time in a distracted state. The top performer spent the majority of time in directed and divided attention states. Unexpectedly, the second-best participant, only 1% worse than the top performer, was distracted almost one third of the experiment, but exhibited a periodic switching strategy, allowing him to pay just enough attention to assist the automation when needed. Indeed, four of the five top performers were distracted more than one-third of the time. These findings suggest that distraction due to boring, low task load environments can be effectively managed through efficient attention switching. Future work is needed to determine optimal frequency and duration of attention state switches given various exogenous attributes, as well as individual variability. These findings have implications for the design of and personnel selection for supervisory control systems where operators monitor highly automated systems for long durations with only occasional or rare input.This work was supported by Aurora Flight Sciences under the ONR Science of Autonomy program as well as the Office of Naval Research (ONR) under Code 34 and MURI [grant number N00014-08-C-070]

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Effects Of Video Game Playing And Training On Unmanned Aerial Vehicle Performance

The popularity of unmanned aerial vehicles (UAVs) has resulted in the need to determine who is suitable to learn to operate UAVs. The present study examined the likelihood that action video game players (VGPs) would make better potential candidates for learning to become UAV pilots. Additional training is also examined as a factor to determine how well training assists with maintaining situational awareness and vigilance during performance of the task, which are beneficial skills for UAV pilots to possess. Ninety-two undergraduate students participated in the study, and piloting skills were tested using the Multi-Attribute Task Battery-II, which consists of generalizations of piloting tasks. VGPs had superior performance on many of the tasks compared to non-video game players, and individuals that received training performed better than those that did not receive training. These findings indicate that VGPs may make a potential candidate group for UAV pilots without needing previous pilot experience

Unmanned Aerial Vehicle (UAV) Operators’ Workload Reduction: The Effect of 3D Audio on Operators’ Workload and Performance during Multi-Aircraft Control

The importance and number of Unmanned Aerial Vehicle (UAV) operations are rapidly growing in both military and civilian applications. This growth has produced significant manpower issues, producing a desire that multiple aircraft are controlled by a single operator as opposed to the current model where one aircraft may require multiple operators. A potential issue is the need for an operator to monitor radio traffic for the call signs of multi-aircraft. An investigation of the use of 3D sound was undertaken to investigate whether an automatic parser, which preselected the spatial location of relevant versus irrelevant call signs, could aid UAV operators in increasing performance with reduced workload. Furthermore, because the 3D audio system may not guarantee 100% reliability, human performance with the 3D audio system was also collected when they were informed announcement that errors were possible and when the reliability level was less than 100%. This investigation included development of a human performance model, simulation of human performance and workload, and a human subject study. Consequently, promising effects of the 3D audio system on multi-aircraft control were found. This novel and unique use of 3D sound is discussed, and significant improvements in response time and workload are demonstrated

Effects of System Reliability and Time Pressure on Autonomous Unmanned Aerial Vehicle Operator Performance and Mental Workload

Unmanned Aerial Vehicles (UAVs) are in the midst of aviation’s next generation. UAVs are being utilized at an increasing rate by military and security operations and are becoming widely popular in usage for activities such as search and rescue and weather research to homeland security and border patrol. In order for a safer integration of UAV’s within the National Airspace System (NAS), many research questions need to be addressed. This study examined the effects of system reliability and time pressure on UAV operator performance and mental workload. Twenty-four undergraduate and graduate students, male and female, from Embry-Riddle Aeronautical University participated in this study. An autonomous UAV simulator was used to engage the participants with a set if prescribed tasks. The primary task measures were image processing time and target acquisition accuracy. Three secondary tasks were concerned with responding to events encountered in typical UAV operations. Using the NASA-Task Load Index (TLX) form, mental workload for UAV autonomous systems was also analyzed. Results showed that system reliability has a significant effect on image processing time, while time pressure produced a significant effect for target acquisition accuracy. A significant effect was also found for the interaction between system reliability and time pressure for pop-up threats that required re-routing processing time. The results were examined and recommendations for future research are discusse

Operator Objective Function Guidance for a Real-time Unmanned Vehicle Scheduling Algorithm

Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that asingle operator can now control multiple heterogeneous unmanned vehicles. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.Aurora Flight Sciences, U.S. Office of Naval Researc