Publication statistics

Pub. period:2010-2012
Pub. count:4
Number of co-authors:8


Number of publications with 3 favourite co-authors:

Jamie C. Macbeth:
M. L. Cummings:
Amit Surana:



Productive colleagues

Luca F. Bertuccelli's 3 most productive colleagues in number of publications:

Krzysztof Z. Gajos:14
M. L. Cummings:10
Fei Gao:3

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Luca F. Bertuccelli


Publications by Luca F. Bertuccelli (bibliography)

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Gao, Fei, Cummings, Missy L. and Bertuccelli, Luca F. (2012): Teamwork in controlling multiple robots. In: Proceedings of the 7th International Conference on Human-Robot Interaction 2012. pp. 81-88.

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.

© All rights reserved Gao et al. and/or their publisher

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Macbeth, Jamie C., Cummings, M. L., Bertuccelli, Luca F. and Surana, Amit (2012): Interface Design for Unmanned Vehicle Supervision through Hybrid Cognitive Task Analysis. In: Proceedings of the Human Factors and Ergonomics Society 2012 Annual Meeting 2012. pp. 2344-2348.

While there is currently significant interest in developing Unmanned Aerial Systems (UASs) that can be supervised by a single operator, the majority of these systems focus on Intelligence, Surveillance, and Reconnaissance (ISR) domains. One domain that has received significantly less attention is the use of multiple UASs to insert or extract supplies or people. To this end, MAVIES (Multi-Autonomous Vehicle Insertion-Extraction System) was developed to allow a single operator the ability to supervise a primary cargo Unmanned Aerial Vehicle (UAV) along with multiple scouting UAVs. This paper will detail the development of the design requirements generated through a Hybrid Cognitive Task Analysis (hCTA) and the display that resulted from these efforts. A major innovation in the hCTA process in this effort was the alteration of the traditional decision ladder process to specifically identify decision-making tasks that must be augmented with automation.

© All rights reserved Macbeth et al. and/or Human Factors and Ergonomics Society

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Jayatilaka, Lahiru G., Bertuccelli, Luca F., Staszewski, James and Gajos, Krzysztof Z. (2011): Evaluating a pattern-based visual support approach for humanitarian landmine clearance. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 453-462.

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.

© All rights reserved Jayatilaka et al. and/or their publisher

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Jayatilaka, Lahiru G., Bertuccelli, Luca F., Staszewski, James and Gajos, Krzysztof Z. (2010): PETALS: a visual interface for landmine detection. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 427-428.

Post-conflict landmines have serious humanitarian repercussions: landmines cost lives, limbs and land. The primary method used to locate these buried devices relies on the inherently dangerous and difficult task of a human listening to audio feedback from a metal detector. Researchers have previously hypothesized that expert operators respond to these challenges by building mental patterns with metal detectors through the identification of object-dependent spatially distributed metallic fields. This paper presents the preliminary stages of a novel interface -- Pattern Enhancement Tool for Assisting Landmine Sensing (PETALS) -- that aims to assist with building and visualizing these patterns, rather than relying on memory alone. Simulated demining experiments show that the experimental interface decreases classification error from 23% to 5% and reduces localization error by 54%, demonstrating the potential for PETALS to improve novice deminer safety and efficiency.

© All rights reserved Jayatilaka et al. and/or their publisher

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