20,763 research outputs found
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
Probabilistic Human-Robot Information Fusion
This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of todayâs robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robotsâ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robotsâ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability
Selecting Metrics to Evaluate Human Supervisory Control Applications
The goal of this research is to develop a methodology to select supervisory control metrics. This
methodology is based on cost-benefit analyses and generic metric classes. In the context of this research,
a metric class is defined as the set of metrics that quantify a certain aspect or component of a system.
Generic metric classes are developed because metrics are mission-specific, but metric classes are
generalizable across different missions. Cost-benefit analyses are utilized because each metric set has
advantages, limitations, and costs, thus the added value of different sets for a given context can be
calculated to select the set that maximizes value and minimizes costs. This report summarizes the
findings of the first part of this research effort that has focused on developing a supervisory control metric
taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus
on applying cost benefit analysis methodologies to metric selection.
Five main metric classes have been identified that apply to supervisory control teams composed
of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency,
human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness
measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency
measure the actions and decisions made by the humans and the automation that compose the team.
Human behavior precursors measure human initial state, including certain attitudes and cognitive
constructs that can be the cause of and drive a given behavior. Collaborative metrics address three
different aspects of collaboration: collaboration between the human and the autonomous platform he is
controlling, collaboration among humans that compose the team, and autonomous collaboration among
platforms. These five metric classes have been populated with metrics and measuring techniques from
the existing literature.
Which specific metrics should be used to evaluate a system will depend on many factors, but as a
rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a
multi-dimensional assessment of the human-automation team. To determine what the impact on our
research has been by not following such a principled approach, we evaluated recent large-scale
supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results
show that prior to adapting this metric classification approach, we were fairly consistent in measuring
mission effectiveness and human behavior through such metrics as reaction times and decision
accuracies. However, despite our supervisory control focus, we were remiss in gathering attention
allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were
redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general
research population in that we tended to gravitate to popular metrics that are relatively easy to gather,
without a clear understanding of exactly what aspect of the systems we were measuring and how the
various metrics informed an overall research question
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