Curtis M. Humphrey

Supporting Wilderness Search and Rescue using a Camera-Equipped Mini UAV

Wilderness Search and Rescue (WiSAR) entails searching over large regions in often rugged remote areas. Because of the large regions and potentially limited mobility of ground searchers, WiSAR is an ideal application for using small (human-packable) unmanned aerial vehicles (UAVs) to provide aerial imagery of the search region. This paper presents a brief analysis of the WiSAR problem with emphasis on practical aspects of visual-based aerial search. As part of this analysis, we present and analyze a generalized contour search algorithm, and relate this search to existing coverage searches. Extending beyond laboratory analysis, lessons from field trials with search and rescue personnel indicated the immediate need to improve two aspects of UAV-enabled search: How video information is presented to searchers and how UAV technology is integrated into existing WiSAR teams. In response to the first need, three computer vision algorithms for improving video display presentation are compared; results indicate that constructing temporally localized image mosaics is more useful than stabilizing video imagery. In response to the second need, a goal-directed task analysis of the WiSAR domain was conducted and combined with field observations to identify operational paradigms and field tactics for coordinating the UAV operator, the payload operator, the mission manager, and ground searchers.

Cognitive Task Analysis for Developing Unmanned Aerial Vehicle Wilderness Search Support

Wilderness search and rescue (WiSAR) requires thousands of hours of search over large and complex terrains. Mini-UAVs (unmanned aerial vehicles) may dramatically improve WiSAR search efficiency. Early field trials in UAV-enabled WiSAR indicated a need to improve the human-UAV interaction, the coordination between the UAV and ground search resources, and the UAV technology. A cognitive task analysis was conducted to inform the design of the UAV technology, the associated interface, and the roles and responsibilities associated with effectively integrating the technology into the existing WiSAR response. Two cognitive task analysis techniques were employed: goal-directed task analysis and a partial cognitive work analysis that included a work domain analysis and a control task analysis. Early field trials and WiSAR search personnel informed the task analyses, which consequently informed the UAV technology design and integration. This paper (a) reviews how and why the task analyses were conducted, how the systems engineering process incorporated field trials to inform the task analyses and to guide the technology development; and (b) provides examples of how the analyses informed the resulting technology development with an eye toward providing insight into how such analysis techniques can be applied to developing UAV systems.

Analysis of complex team-based systems: augmentations to goal-directed task analysis and cognitive work analysis

This research modified two analysis techniques, goal-directed task analysis (GDTA) and modified cognitive work analysis (mCWA), in order to apply them to the analysis of a complex team-based system. Traditionally, GDTA and mCWA have been applied to narrowly focused physical-based domains; however, the chemical, biological, radiological, nuclear and explosive (CBRNE) incident response domain is a very complex domain in which individual humans and teams of humans with specialised skills and responsibilities are considered to be system components. The resulting system combines very large number of components, decision-makers and uncertain environmental issues. Each analysis technique provided strengths that complemented the others weaknesses; however, the analysis techniques required augmentations in order to facilitate information capture and correspondence to design requirements. The contributions of this article include a presentation of the augmentations and a discussion of the benefits provided by employing both analysis techniques.

Visualization of Multiple Robots during Team Activities

As robotic systems encompass larger numbers of individual robotic agents, interface design must provide better visual representations that account for factors affecting the human operators situational awareness. This work investigates three robotic team visualizations via an evaluation with sixteen participants who either had used robots or had no experience with robots. The team visualizations varied in how much information was displayed: only individual robots, individual robots connected via a semitransparent team shape, or a solid team shape. The evaluation results revealed that the two visualizations utilizing a team shape were used more frequently than the visualization displaying only individual robots.

General Visualization Abstraction Algorithm for Directable Interfaces: Component Performance and Learning Effects

Prior results demonstrated that the general visualization abstraction (GVA) algorithm can perform information abstraction (i.e., selection and grouping) and determine how information items should be presented (i.e., size) while lowering workload and improving situational awareness and task performance. This paper presents results from a within-subject evaluation to ascertain the relative strengths and weaknesses of the GVA algorithms components and associated learning effects. The results corroborate the previous results and demonstrate that the GVA algorithms underlying subcomponent structural composition is beneficial. Furthermore, these results indicate that usage of the GVA algorithm requires some learning before the benefits are achieved.

Compass visualizations for human-robotic interaction

Compasses have been used for centuries to express directions and are commonplace in many user interfaces; however, there has not been work in human-robotic interaction (HRI) to ascertain how different compass visualizations affect the interaction. This paper presents a HRI evaluation comparing two representative compass visualizations: top-down and in-world world-aligned. The compass visualizations were evaluated to ascertain which one provides better metric judgment accuracy, lowers workload, provides better situational awareness, is perceived as easier to use, and is preferred. Twenty-four participants completed a within-subject repeated measures experiment. The results agreed with the existing principles relating to 2D and 3D views, or projections of a three-dimensional scene, in that a top-down (2D view) compass visualization is easier to use for metric judgment tasks and a world-aligned (3D view) compass visualization yields faster performance for general navigation tasks. The implication for HRI is that the choice in compass visualization has a definite and nontrivial impact on operator performance (world-aligned was faster), situational awareness (top-down was better), and perceived ease of use (top-down was easier).

Cognitive information flow analysis

The cognitive information flow analysis (CIFA) is introduced as a method to integrate results from cognitive task and work analyses in order to provide a focus on the necessary system information flow, which includes how information is produced, consumed, and transformed by the various system functions and users. CIFA can be used as a tool to augment cognitive task and work analyses. This paper presents the CIFA technique, provides a case study that applies the CIFA method to existing goal-directed task analysis and modified cognitive work analysis results, and provides insight into CIFA’s use for informing the design of a human-robot system. CIFA augments the results provided by cognitive task and work analyses and can guide system design and development. CIFA differs from existing information flow techniques in that it allows representation of systems containing large numbers of users for highly complex and uncertain domains. Existing cognitive task and work analyses integration methods rely heavily on relational tables. CIFA specifically expresses the interconnectivity of the various system subcomponents, including partial ordering and parallelism, by fundamentally focusing on the information flow. CIFA also identifies both existing and potential, information bottlenecks and highlights teamwork.

A human eye like perspective for remote vision

Robots in remote environments (e.g., emergency response) have many potential benefits and affordances, with imagery (or video) being a major, if not primary, affordance. However, remote imagery is usually affected by the keyhole effect, or viewing the world through a “soda straw.” This work focuses on reducing the keyhole effect by improving the viewing angle of the imagery using a novel method that produces results more akin to that provided by the human vision system. The method and early results are subsequently presented for this human eye like perspective for remote vision.

General Visualization Abstraction Algorithm for Geographic Map-based Directable Interfaces

Emergency incident response, including Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) a.k.a. weapons of mass destruction incidents, is evolving from a response involving humans with equipment to a response system combining humans and thinking machines (e.g. robots). The robots, along with possibly other deployed sensors, will generate and capture volumes of information that will be presented in directable visualizations. This paper presents a novel approach to performing information abstraction (i.e., selection and grouping) and determining how each information item should be presented (i.e., its shape) in directable visualizations. This new approach employs the General Visualization Abstraction (GVA) algorithm to make salient and direct attention to the most relevant information items by determining an importance value for each information item based on the items relationship with two classes of information: historically relevant and currently relevant information, and novel and emerging information.

Human roles for robot augmented first response

Emergency incident response varies dramatically based on incident type, size, and response duration. Robots will facilitate response planning and maintaining awareness, remove responders from dangerous situations, and allow for immediate site feedback prior to human responder entry. Introducing new robots to emergency incident response can affect the human responder command hierarchys workflow, decision-making, and responsibilities. Thus, understanding the relationship and interaction roles humans in the response system can assume when robots are added is critical. Large-scale emergency incidents (or response systems) can involve thousands of responders and many thousands of civilians and victims. The primary contribution is the classification, based on five components, of response system individuals to ten roles that encapsulate the hundreds of potential responders, bystanders and victims. A new role, the abstracted supervisor is also defined.