Operator Scheduling Strategies in Supervisory Control of Multiple UAVs

The application of network centric operations to time-constrained command and control environments will mean that human operators will be increasingly responsible for multiple simultaneous supervisory control tasks. One such futuristic application will be the control of multiple unmanned aerial vehicles (UAVs) by a single operator. To achieve such performance in complex, time critical, and high risk settings, automated systems will be required both to guarantee rapid system response as well as manageable workload for operators. Through the development of a simulation test bed for human supervisory control of multiple independent UAVs by a single operator, this paper presents recent efforts to investigate workload mitigation strategies as a function of increasing automation. A humanin- the-loop experiment revealed that under low workload conditions, operators’ cognitive strategies were relatively robust across increasing levels of automated decision support. However, when provided with explicit automated recommendations and with the ability to negotiate with external agencies for delays in arrival times for targets, operators inappropriately fixated on the need to globally optimize their schedules. In addition, without explicit visual representation of uncertainty, operators tended to treated all probabilities uniformly. This study also revealed that operators that reached cognitive saturation adapted two very distinct management strategies, which led to varying degrees of success. Lastly, operators with management-by-exception decision support exhibited evidence of automation bias.This research was sponsored by Boeing Phantom Works

Global vs. local decision support for multiple independent UAV schedule management

As unmanned aerial vehicles (UAVs) become increasingly autonomous, time-critical and complex single-operator systems will require advance prediction and mitigation of schedule conflicts. However, actions that mitigate a current schedule conflict may create future schedule problems. Decision support is needed allowing an operator to evaluate different mission schedule management options in real-time. This paper describes two decision support visualisations for single-operator supervisory control of four independent UAVs performing a time-critical targeting mission. A configural display common to both visualisations, called StarVis, graphically depicts current schedule problems, as well as projections of potential local and global schedule problems. Results from an experiment showed that subjects using the locally optimal StarVis implementation had better performance, higher situational awareness, and no significant increase in workload over a more globally optimal implementation of StarVis. This research effort highlights how the same decision support design applied at different abstraction levels can produce different performance results.This research was sponsored by Mitre, Inc

The Impact of Intelligent Aiding for Multiple Unmanned Aerial Vehicle Schedule Management

There is increasing interest in designing systems such that the current many-to-one ratio of operators to unmanned vehicles (UVs) can be inverted. Instead of lower-level tasks performed by today’s UV teams, the sole operator would focus on high-level supervisory control tasks. A key challenge in the design of such single-operator systems will be the need to minimize periods of excessive workload that arise when critical tasks for several UVs occur simultaneously. Thus some kind of decision support is needed that facilitates an operator’s ability to evaluate different action alternatives for managing a multiple UV mission schedule in real-time. This paper describes two decision support experiments that attempted to provide UAV operators with multivariate scheduling assistance, with mixed results. Those automated decision support tools that provided more local, as opposed to global, visual recommendations produced superior performance, suggesting that meta-information displays could saturate operators and reduce performance.This research was sponsored by Boeing Phantom Work and Mitre, Inc

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

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc

,The Impact of Human-Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

For future systems that require one or a small team of operators to supervise a network of automated agents, automated planners are critical since they are faster than humans for path planning and resource allocation in multivariate, dynamic, time-pressured environments. However, such planners can be brittle and unable to respond to emergent events. Human operators can aid such systems by bringing their knowledge-based reasoning and experience to bear. Given a decentralized task planner and a goal-based operator interface for a network of unmanned vehicles in a search, track, and neutralize mission, we demonstrate with a human-on-the-loop experiment that humans guiding these decentralized planners improved system performance by up to 50%. However, those tasks that required precise and rapid calculations were not significantly improved with human aid. Thus, there is a shared space in such complex missions for human–automation collaboration

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 Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems

Recent studies have shown that with appropriate operator decision support and with sufficient automation, inverting the multiple operators to single-unmanned 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 discrete event simulation (DES) model as a means to model a single operator supervising multiple heterogeneous unmanned vehicles. The DES model can be used to understand the impact of varying both vehicle team design variables (such as team composition) and operator design variables (including attention allocation strategies). The model also highlights the sub-components of operator attention allocation schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. Results from an experimental case study are then used to validate the model, and make predictions about operator performance for various heterogeneous team configurations.The research was supported by Charles River Analytics, the Office of Naval Research (ONR), and MIT Lincoln Laboratory

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

Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human–automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative