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For complex systems that embed automation, but also rely on human interaction for guidance and contingency management, holistic models are needed that provide for an understanding of the individual human and computer elements, and address the critical interactions of such complex systems. Discrete event simulation (DES) models and system dynamics (SD) models are two different approaches that can be used to address these requirements. Both modelling approaches can support the designers of future autonomous vehicle (AV) systems by simulating the impact of alternate designs on vehicle, operator, and system performance. However, the DES modelling approach is likely best suited for using probabilistic distributions to accurately model an operator who is a serial processor of discrete tasks, as well as an environment with randomly occurring events. The SD modelling approach is better suited for modelling continuous performance feedback that is temporally dependent and is affected by qualitative variables such a...

Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that a single 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.

The development of Unmanned Vehicles (UVs) with increasing autonomy has enabled a transition from teleoperation to Human Supervisory Control (HSC). In this demonstration, participants can test three innovative operator interfaces for HSC of UVs. The first system allows users to control a Micro Air Vehicle (MAV) via a hand-held device, such as an iPhone®, through high-level waypoint commands and fine-grained nudge controls. The second system enables a single operator to collaborate with an automated planner to control multiple heterogeneous UVs from a laptop-sized display for searching for, tracking and engaging moving ground targets. The third system is designed to aid in planning on naval aircraft carrier decks, serving as a decision support tool for a supervisor overseeing and scheduling the activity of people, vehicles and unmanned vehicles working in this complex and uncertain environment.

Objective: We examined the impact of priming on operator trust and system performance when supervising a decentralized network of heterogeneous unmanned vehicles (UVs). Background: Advances in autonomy have enabled a future vision of single-operator control of multiple heterogeneous UVs. Real-time scheduling for multiple UVs in uncertain environments requires the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Because of system and environmental uncertainty, appropriate operator trust will be instrumental to maintain high system performance and prevent cognitive overload. Method: Three groups of operators experienced different levels of trust priming prior to conducting simulated missions in an existing, multiple-UV simulation environment. Results: Participants who play computer and video games frequently were found to have a higher propensity to overtrust automation. By priming gamers to lower their initial trust to a more appropriate level, system performance was improved by 10% as compared to gamers who were primed to have higher trust in the automation. Conclusion: Priming was successful at adjusting the operator’s initial and dynamic trust in the automated scheduling algorithm, which had a substantial impact on system performance. Application: These results have important implications for personnel selection and training for futuristic multi-UV systems under human supervision. Although gamers may bring valuable skills, they may also be potentially prone to automation bias. Priming during training and regular priming throughout missions may be one potential method for overcoming this propensity to overtrust automation.

This paper presents the outdoor ight test results of a decentralized multi-UAV system supervised by a human operator. The system balances the roles of the human operator and the UAV autonomous behaviors with the objective of maximizing the execution performance. The operator manages the mission by inputting and modifying tasks instead of controlling individual UAVs. The Consensus-Based Bundle Algorithm (CBBA) is used as a real-time, scalable, dynamic multi-agent multi-task planning algorithm to allocate tasks approved by the operator to UAVs. A team of three quadrotors and one xed wing UAV collaborated in an operationally relevant scenario supporting a cargo UAV resupply mission. Thirteen of fourteen multi-UAV outdoor ight test trials successfully accomplished the mission objectives. The framework was shown to be robust to system failures and degradations commonly encountered during eld testing primarily because of health monitoring and management tools that were incorporated in the design. Instances of task allocation and path planning churning were observed which are linked to uncertainties of operating outdoors. Lessons learned during ight test operations are highlighted as they are relevant to other similar types of systems and missions.

Advances in autonomy have made it possible to invert the typical operator-to-unmanned vehicle ratio so that a single operator can now control multiple heterogeneous Unmanned Vehicles (UVs). Real-time scheduling and task assignment for multiple UVs in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors through mixed-initiative systems. The goal of this paper is to analyze the interactions between operators and scheduling algorithms in two human- in-the-loop multiple UV control experiments. The impact of real-time operator modifications to the objective function of an optimization algorithm for multi-UV scheduling is described. Results from outdoor multiple UV flight tests using a human-computer collaborative scheduling system are presented, which provide valuable insight into the impact of environmental uncertainty and vehicle failures on system effectiveness.

Real-time scheduling and task assignment for multiple Unmanned Vehicles (UVs) in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Identifying the characteristics that make a scheduling algorithm suitable for human-computer collaboration is essential for the development of an effective scheduling system. This high-level systems analysis paper begins the process of deriving requirements for collaborative scheduling algorithms by conducting a survey of 117 publications within the past five years in academia and industry on multiple UV scheduling algorithms. The goal of the survey is to determine the types and frequency of scheduling algorithms that are currently in use and to classify the characteristics and capabilities of these algorithms. Results show that academia has settled on meta-heuristic and auction-based algorithms as providing the best balance of performance and computational speed. In industry, however, the most widely used solution methods are “iterative” approaches that monotonically improve the schedule with further iterations. Industry-developed algorithms are more likely to be capable of scheduling heterogeneous UVs, but university researchers have developed more algorithms that can account for uncertainty and provide estimates of robustness. The different objectives of industry practitioners and academic researchers may be driving these disparities. Addressing this gap will be essential to the development and adoption of future humancomputer collaborative scheduling systems.

A future concept of operations for co ntrolling unmanned vehicles has a single, forwarddeployed soldier supervising multiple, heterogeneous unmanned vehicles. This operator would collaborate with an automated planner that presents the operator with a choice of potential plans for how the unmanned vehicles could efficiently search the dynamic environment for new targets, track identified targets, and destroy hostile targets. A key step towards this future concept is to determine how often the automated planner should provide the operator with potential plans without causing a detrimentally high workload for the operator, which could have negative performance consequences. This research describes the development of the Onboard Planning System for Unmanned Vehicles Supporting Expeditionary Reconnaissance and Surveillance (OPS-USERS) testbed and an experiment to measure operator workload and performance in a set of scenarios with different replanning intervals. The results show that the replan interval has a significant effect on workload and performance with significantly lower performance at the lowest replan interval . Replanning too frequently in a human-on-the-loop collaboration can cause higher levels of workload and lower performance. Further research is required to determine the impact o f low workload in a highly autonomous system on overall performance. Also, operator strategy and trust in automation with the OPS-USERS system deserves further research.

Sonic booms are a barrier to wider application of supersonic flight due to their harmful effect on humans, animals, and structures. This research presents an empirical formulation for rapid prediction of the magnitude of a sonic boom overpressure using linear regression of independent parameter groups operating on the Lee and Downing 1991 database of measurements of sonic booms created by military aircraft at Edwards Air Force Base. This formulation, originally developed as a student project, employs an empirical F function that characterizes the nearfield effects of shape, lift, and Mach number on the sonic boom. The prediction accuracy, as assessed by the scatter within the original database, is well within the resolution capability of the human ear, and produces correlations very much superior to alternative analytical prediction models. Further development led to an expression for the distribution of sonic boom overpressure lateral to the plane of the flight path. The entire method was incorporated into a user-friendly Matlab® Graphical User Interface that predicts the magnitude of the sonic boom overpressure and creates an animated graphical portrayal of the shock wave and sonic boom footprint. This paper presents additional work on the method by employing data from the 1960's on several aircraft that are different from those in the formulation database. Good agreement was found with two new conventional configurations, but significant adjustments to the recognition of wing area in the shape and lift factors are necessary to produce satisfactory results for the XB-70 because of its unique use of folding wingtips and compression lift. Because this method is presented as a simple linear algebraic formula for the overpressure, it can be used easily to screen new low-boom supersonic transport designs, to determine the environmental impact of a supersonic flight, and to educate the public about sonic booms.