Modeling Contingency Management in Unmanned Aircraft Systems Traffic Management

Contingency management in future Unmanned Aerial Vehicles (UAVs) Traffic Management (UTM) requires a variety of distributed and interdependent functions and services—such as flight tracking and conformance monitoring, weather detection and prediction, and ground-based detection and avoidance— that need to be coordinated across multiple roles and organizations. This paper describes a combination of cognitive walkthroughs and computational modeling of work to analyze edge case scenarios and assess resiliency in future UTM operations. We discuss how the walkthrough and modeling inform each other and present early results. The ultimate goal of this work is to identify requirements for robust and resilient system responses in future UTM contingency management

Computational simulation of adaptation of work strategies in human-robot teams

Human-robot teams operating in complex work domains, such as space operations, need to adapt to maintain performance under a wide variety of work conditions. This thesis argues that from the start team design needs to establish team structures that allow flexibility in strategies for conducting the team’s collective work. In addition, team design needs to facilitate fluent coordination of work, fostering the interweaving of team members’ dependent actions in ways that accounts for the dynamic characteristics of the work and the work environment. This thesis establishes a methodology to analyze a team’s strategies based on computational modeling of a team’s collective work, including the teamwork required to coordinate dependent work between multiple team members. This approach consists of the systematic identification of feasible work strategies and the simulation of work models to address the dynamic and emergent nature of a team’s work. It provides a formative analysis tool to help designers predict and understand the effects of their design choices on a team’s feasible work strategies. Two case studies on space operations demonstrate how this approach can predict how work allocation and human-robot interaction modes can foster and/or limit the availability of appropriate work strategies.Ph.D

Computational Simulation of Authority-Responsibility Mismatches in Air-Ground Function Allocation

Authority-responsibility mismatches are created when one agent is authorized (has authority) to perform an activity, but a different agent is responsible for its outcome. An authority-responsibility mismatch demands monitoring by the responsible agent that itself requires additional information transfer and taskload. This paper demonstrates a computational simulation methodology that identifies when mismatches will occur in complex, multi-agent aviation operations, and their implications for information transfer between agents and task demands on each agent. A case study examines 25 authority and responsibility allocations in a NextGen/SESAR scenario in a terminal area where authority and responsibility for activities involving optimal profile descents, merging and spacing can be fluidly allocated to the aircraft (pilot/flight management system) or to the ground (air traffic controller/controller decision aids and automation)

Work Dynamics of Task Work and Teamwork in Function Allocation for Manned Spaceflight Operations

This paper proposes a methodology for human-robot function allocation for future manned space exploration missions that uses fast-time computational simulation. Dynamics of taskwork and teamwork often result in emergent work patterns that are difficult to predict from static analysis of function allocations. Wemodel thedynamics of taskwork and teamwork and demonstrate our approach through a case study that explores the function allocation design space for an on-orbit maintenancemissioninvolving humans and various robots. The case study highlightsthe method’s ability to predict possible concerns associated with limited availability of physical resources, action interdependencies, and communication requirements with possible time delays, and shows the influence of work dynamics on missionperformance

Evaluation of a Decision-Based Invocation Strategy for Adaptive Support for Air Traffic Control

Air traffic controller workload is a limiting factor in the current air traffic management system. Adaptive support systems have the potential to balance controller workload and gain acceptance as they provide support during times of need. Challenges in the design of adaptive support systems are to decide when and how to trigger support. The goal of this study is to gain empirical insights into these challenges through a human-in-the-loop experiment, featuring a simplified air traffic control environment in which a novel triggering mechanism uses the quality of the controller's decisions to determine when support is needed. The designed system seeks to prevent high workload conditions by providing resolution advisories when the controller exceeds a threshold of 'self-complicating' decisions. Results indicate that the new system is indeed capable of increasing the efficiency and safety compared to full manual control without intervention. More adaptive support, however, increased the frustration of participants, decreased acceptance, and did not result in improved workload ratings. These findings suggest that, unless we can better infer human intent in complex work environments, adaptive support at the level of decision-making is problematic. A potentially more fruitful direction is to provide support at the level of information integration, with full decision-making authority with the human.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

Evaluating Envisioned Air Mobility Architectures Using Computational Simulations of Work

Urban Air Mobility (UAM) is an envisioned concept of operation for managing uncrewed and crewed flights for urban, regional, and interregional air transportation. One element of further development of this envisioned system is to specify architectures in terms of roles and procedures for managing contingencies. Contingency management is a highly distributed function involving coordination between multiple system actors. In this study, a computational model is applied to analyze envisioned procedures and identify architectural solutions to improve the robustness of the contingency response. The simulation framework Work Models that Compute (WMC) is used to analyze a proposed UAM lost link procedure in the Dallas-Fort Worth (DFW) airspace while varying task design and control authority of operators, service providers, pilots, and vertiport operators. The simulations provide insights into how candidate designs align or misalign with the dynamics of the contingency. This approach can improve the design and verification of procedures in similar envisioned operations

Adaptive Automation Based on Air Traffic Controller Decision-Making

Through smart scheduling and triggering of automation support, adaptive automation has the potential to balance air traffic controller workload. The challenge in the design of adaptive automation systems is to decide how and when the automation should provide support. This paper describes the design of a novel mechanism for adaptively invoking automation support. Whereas most adaptive automation support systems are reactive in that they invoke automation support after controller workload has increased, the aim of the designed mechanism is to proactively trigger automation support prior to workload increases. To do this, the mechanism assesses the quality of air traffic controller's decisions. The designed adaptive automation system has been tested in a human-in-the-loop experiment. Results indicate that the adaptive support helps to increase efficiency and safety as compared to manual control. However, lower triggering thresholds (resulting in more frequent automation intervention) increased the frustration level of participants (as measured with NASA TLX) and decreased acceptance of the support.Control & SimulationControl & Operation