RoboCon: A general purpose telerobotic control center

This report describes human factors issues involved in the design of RoboCon, a multi-purpose control center for use in US Department of Energy remote handling applications. RoboCon is intended to be a flexible, modular control center capable of supporting a wide variety of robotic devices

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025-1700 000 km²) and below-ground carbon storage (105-288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km² (95th percentile confidence interval: 128 671-373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin

Manipulator Performance Evaluation Using Fitts' Taping Task

Metaphorically, a teleoperator with master controllers projects the user's arms and hands into a re- mote area, Therefore, human users interact with teleoperators at a more fundamental level than they do with most human-machine systems. Instead of inputting decisions about how the system should func- tion, teleoperator users input the movements they might make if they were truly in the remote area and the remote machine must recreate their trajectories and impedance. This intense human-machine inter- action requires displays and controls more carefully attuned to human motor capabilities than is neces- sary with most systems. It is important for teleoperated manipulators to be able to recreate human trajectories and impedance in real time. One method for assessing manipulator performance is to observe how well a system be- haves while a human user completes human dexterity tasks with it. Fitts' tapping task has been, used many times in the past for this purpose. This report describes such a performance assessment. The International Submarine Engineering (ISE) Autonomous/Teleoperated Operations Manipulator (ATOM) servomanipulator system was evalu- ated using a generic positioning accuracy task. The task is a simple one but has the merits of (1) pro- ducing a performance function estimate rather than a point estimate and (2) being widely used in the past for human and servomanipulator dexterity tests. Results of testing using this task may, therefore, allow comparison with other manipulators, and is generically representative of a broad class of tasks. Results of the testing indicate that the ATOM manipulator is capable of performing the task. Force reflection had a negative impact on task efficiency in these data. This was most likely caused by the high resistance to movement the master controller exhibited with the force reflection engaged. Measurements of exerted forces were not made, so it is not possible to say whether the force reflection helped partici- pants control force during testing

Distributed Regression For Heterogeneous Data Sets

Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they can be successfully used in distributed scenarios with context heterogeneity