To become proficient at landmine detection, novice deminers need to master several kinds of skills: the proper physical operation of the metal detector, the interpretation of the metal detector auditory feedback, and the abstract skill of constructing and interpreting mental representations of the "metallic signatures" produced by the buried objects. This last skill is particularly useful for safely dealing with mines laid out in cluster configurations, where their metallic signatures overlap and thus a danger exists that a deminer might either miss some of the mines or incorrectly assess their exact positions. However, some novice deminers find it challenging to learn how to properly reason about metallic signatures. We have developed Petals, a system that explicitly visualizes a trainee's metal detector operation history on a training task as well as the edge points of the metallic signatures that the trainee collected. Petals enables instructors to supervise multiple trainees at a time, to assess their performance at a glance, and to provide immediate and specific feedback both on the correctness of their final judgements about the number and positions of landmines, and on the process through which they arrived at their conclusions. The results of our field evaluations at the Humanitarian Demining Training Center showed that both the instructors and the trainees found the system a valuable addition to the training course. The results of a controlled study demonstrated that trainees who had access to Petals during training made significantly fewer errors (6% error rate) on relevant tasks during the final exam (which was conducted without Petals) than trainees who did not have access to Petals during training (those participants had a 21% error rate).

Augmented reality (AR) is a live, direct or indirect, view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data [1]. AR technology has seen the advent of smaller form factors, more powerful processors, higher resolution cameras, and distributed computation; the advent of technology like Google Glass and Epson Moverio BT-200 have generated renewed interest in the commercial domains. This new eyewear also extends the earlier capabilities of optical-/video-see through glasses with Bluetooth, Wifi, and 3G connectivity to remote databases. While there are as yet many significant technical hurdles for industry-specific AR systems that must be surmounted to ensure efficient operation, there are still numerous user-centric issues that still need to be addressed to enable the desired safety and efficiency potential of the technology itself. The issues themselves are plentiful, including ergonomic issues (size, weight of the augmented reality hardware, when dealing with glasses), user interface requirements (font sizes, lighting conditions impacting the legibility of text or the rendering of the digital content, interaction with tablets or glasses), and physiological issues (eye fatigue, user perception due to latency of content rendering, increased user workload). Our contribution in this paper is the presentation of an ongoing development of a user-centric, industrial testbed devoted to the requirements gathering, development, and assessment of AR technologies and presentation of two sample use cases. Our testbed is designed to investigate usability research questions related to AR, and this contribution constitutes some of our work in progress in developing this vision.

There has recently been a significant amount of activity in developing supervisory control algorithms for multiple unmanned aerial vehicle operation by a single operator. While previous work has demonstrated the favorable impacts that arise in the introduction of increasingly sophisticated autonomy algorithms, little work has performed an explicit comparison of different types of multiple unmanned vehicle control architectures on operator performance and workload. This paper compares a vehicle-based paradigm (where a single operator individually assigns tasks to unmanned assets) to a task-based paradigm (where the operator generates a task list, which is then given to the group of vehicles that determine how to best divide the tasks among themselves.) The results demonstrate significant advantages in using a task-based paradigm for both overall performance and robustness to increased workload. This effort also demonstrated that while previous video gaming experience mattered for performance, the degree of experience that demonstrated benefit was minimal. Further work should focus on designing a flexible automated system that allows operators to focus on a primary goal, but also facilitate lower level control when needed without degradation in performance.

Simultaneously controlling increasing numbers of robots requires multiple operators working together as a team. Helping operators allocate attention among different robots and determining how to construct the human-robot team to promote performance and reduce workload are critical questions that must be answered in these settings. To this end, we investigated the effect of team structure and search guidance on operators' performance, subjective workload, work processes and communication. To investigate team structure in an urban search and rescue setting, we compared a pooled condition, in which team members shared control of 24 robots, with a sector condition, in which each team member control half of all the robots. For search guidance, a notification was given when the operator spent too much time on one robot and either suggested or forced the operator to change to another robot. A total of 48 participants completed the experiment with two persons forming one team. The results demonstrate that automated search guidance neither increased nor decreased performance. However, suggested search guidance decreased average task completion time in Sector teams. Search guidance also influenced operators' teleoperation behaviors. For team structure, pooled teams experienced lower subjective workload than sector teams. Pooled teams communicated more than sector teams, but sector teams teleoperated more than pool teams.

Unmanned aerial vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models that can be used to develop assistive decision support for UAV operators involved in these complex search missions. Previous human-in-the-loop experiments have shown that operator detection probabilities may decay with increased search time. Providing the operators with the ability to requeue difficult images with the option of relooking at targets later was hypothesized to help operators improve their search accuracy. However, it was not well understood how mission performance could be impacted by operators performing requeues with multiple UAVs. This work extends a queuing model of the human operator by developing a retrial queue model (ReQM) that mathematically describes the use of relooks. We use ReQM to generate performance predictions through discrete event simulation. We validate these predictions through a human-in-the-loop experiment that evaluates the impact of requeuing on a simulated multiple-UAV mission. Our results suggest that, while requeuing can improve detection accuracy and decrease mean search times, operators may need additional decision support to use relooks effectively.

Unexploded landmines have severe post-conflict humanitarian repercussions: landmines cost lives, limbs and land. For deminers engaged in humanitarian landmine clearance, metal detectors remain the primary detection tool as more sophisticated technologies fail to get adopted due to restrictive cost, low reliability, and limited robustness. Metal detectors are, however, of limited effectiveness, as modern landmines contain only minimal amounts of metal, making them difficult to distinguish from the ubiquitous but harmless metallic clutter littering post-combat areas. We seek to improve the safety and efficiency of the demining process by developing support tools that will enable deminers to make better decisions using feedback from existing metal detectors. To this end, in this paper we propose and evaluate a novel, pattern-based visual support approach inspired by the documented strategies employed by expert deminers. In our laboratory study, participants provided with a prototype of our support tool were 80% less likely to mistake a mine for harmless clutter. A follow-up study demonstrates the potential of our pattern-based approach to enable peer decision-making support during landmine clearance. Lastly, we identify several design opportunities for further improving deminers' decision making capabilities.