Interruptions have negative effects on the task performance in modern work environments. These negative effects are not affordable in tasks in which decisions are time-critical and have a life-critical nature. Human-supervisory control (HSC) tasks in time-critical settings such as mission command and control and emergency response are especially vulnerable to the negative effects of interruptions since supervisors in these settings are prone to frequent interruptions which are valuable source of information and hence cannot be ignored and consequences of a wrong decision in these settings is very costly because of their life-critical nature. To address this issue, this thesis investigates an activity-centric design approach that aims to help team supervisors in a complex mission control operation to remain aware of the activities that most likely would affect their decisions, while minimizing disruption. An interruption recovery assistant (IRA) tool was designed to promote activity and situation awareness of a team of UAV operators in a representative task. Initial pilot studies showed a positive trend in effectiveness of the IRA tool on recovery time and decision accuracy. This thesis explores alternative design approaches to validate the effectiveness of an interruption recovery tool that enable mission commanders rapidly and effectively regain the situational awareness after an interruption occurs in the mission environment. This thesis overview these design approaches and present results from a series of formative evaluations of our prototype designs. These evaluations were conducted in an experimental platform designed to emulate futuristic semi-autonomous UAV team mission operations.

Interruptions have negative effects on the task performance in modern work environments. These negative effects are not affordable in tasks in which decisions are time-critical and have a life-critical nature. Human-supervisory control (HSC) tasks in time-critical settings such as mission command and control and emergency response are especially vulnerable to the negative effects of interruptions since supervisors in these settings are prone to frequent interruptions which are valuable source of information and hence cannot be ignored and consequences of a wrong decision in these settings is very costly because of their life-critical nature. To address this issue, this thesis investigates an activity-centric design approach that aims to help team supervisors in a complex mission control operation to remain aware of the activities that most likely would affect their decisions, while minimizing disruption. An interruption recovery assistant (IRA) tool was designed to promote activity and situation awareness of a team of UAV operators in a representative task. Initial pilot studies showed a positive trend in effectiveness of the IRA tool on recovery time and decision accuracy. This thesis explores alternative design approaches to validate the effectiveness of an interruption recovery tool that enable mission commanders rapidly and effectively regain the situational awareness after an interruption occurs in the mission environment. This thesis overview these design approaches and present results from a series of formative evaluations of our prototype designs. These evaluations were conducted in an experimental platform designed to emulate futuristic semi-autonomous UAV team mission operations.

This dissertation models human intent for a robot navigation task, managed by a human and undertaken by a robot in a dynamic, multi-objective environment. Intent is expressed by a human through a user interface and then translated into a robot trajectory that satisfies a set of human-specified objectives and constraints. For a goal-based robot navigation task in a dynamic environment, intent includes expectations about a path in terms of objectives and constraints to be met. If the planned path drifts from the human's intent as the environment changes, a new path needs to be planned. The intent framework has four elements: (a) a mathematical representation of human intent within a multi-objective optimization problem; (b) design of an interactive graphical user interface that enables a human to communicate intent to the robot and then to subsequently monitor intent execution; (c) integration and adoption of a fast online path-planning algorithms that generate solutions/trajectories conforming to the given intent; and (d) design of metric-based triggers that provide a human the opportunity to correct or adapt a planned path to keep it aligned with intent as the environment changes. Key contributions of the dissertation are: (i) design and evaluation of different user interfaces to express intent, (ii) use of two different metrics, cosine similarity and intent threshold margin, that help quantify intent, and (iii) application of the metrics in path (re)planning to detect intent mismatches for a robot navigating in a dynamic environment. A set of user studies including both controlled laboratory experiments and Amazon Mechanical Turk studies were conducted to evaluate each of these dissertation components.

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