A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations

Machine Learning-based Image Forgery Detection

openImage manipulation tools are constantly improving. Most recently, the productization of generative models in popular software like Adobe Photoshop provided a whole new range of possibilities. Though many applications might be harmless, image forgery is not. Tampered images can spread false information, manipulate opinions, and erode trust in media. Therefore, being able to detect fake images is of the utmost importance. The majority of Image Forgery Detection models consist of specialized architectures, often trained with limited data and computational resources. In contrast, image segmentation has found substantial interest and investment. In this work, I explore the capabilities of state-of-the-art general image segmentation models to adapt to the task of Image Forgery Detection to leverage the extensive resources and advancements in this field. I assess their performance on the detection of classical Photoshop manipulation like splicing. Further, I extend the scope to the detection of AI-inpainted images, i.e. images that were manipulated using deep generative models. I show that image segmentation models can keep up with state-of-the-art forgery detection tools. Moreover, the models can detect AI-inpainted regions by identifying the characteristic frequency signature of the generative models

Planning Flight Paths of Autonomous Aerobots

Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions

Graph-Based Path-Planning for Titan Balloons

A document describes a graph-based path-planning algorithm for balloons with vertical control authority and little or no horizontal control authority. The balloons are designed to explore celestial bodies with atmospheres, such as Titan, a moon of Saturn. The algorithm discussed enables the balloon to achieve horizontal motion using the local horizontal winds. The approach is novel because it enables the balloons to use arbitrary wind field models. This is in contrast to prior approaches that used highly simplified wind field models, such as linear, or binary, winds. This new approach works by discretizing the space in which the balloon operates, and representing the possible states of the balloon as a graph whose arcs represent the time taken to move from one node to another. The approach works with arbitrary wind fields, by looking up the wind strength and direction at every node in the graph from an arbitrary wind model. Having generated the graph, search techniques such as Dijkstra s algorithm are then used to find the set of vertical actuation commands that takes the balloon from the start to the goal in minimum time. In addition, the set of reachable locations on the moon or planet can be determined

Global-referenced navigation grids for off-road vehicles and environments

[EN] The presence of automation and information technology in agricultural environments seems no longer questionable; smart spraying, variable rate fertilizing, or automatic guidance are becoming usual management tools in modern farms. Yet, such techniques are still in their nascence and offer a lively hotbed for innovation. In particular, significant research efforts are being directed toward vehicle navigation and awareness in off-road environments. However, the majority of solutions being developed are based on occupancy grids referenced with odometry and dead-reckoning, or alternatively based on GPS waypoint following, but never based on both. Yet, navigation in off-road environments highly benefits from both approaches: perception data effectively condensed in regular grids, and global references for every cell of the grid. This research proposes a framework to build globally referenced navigation grids by combining three-dimensional stereo vision with satellite-based global positioning. The construction process entails the in-field recording of perceptual information plus the geodetic coordinates of the vehicle at every image acquisition position, in addition to other basic data as velocity, heading, or GPS quality indices. The creation of local grids occurs in real time right after the stereo images have been captured by the vehicle in the field, but the final assembly of universal grids takes place after finishing the acquisition phase. Vehicle-fixed individual grids are then superposed onto the global grid, transferring original perception data to universal cells expressed in Local Tangent Plane coordinates. Global referencing allows the discontinuous appendage of data to succeed in the completion and updating of navigation grids along the time over multiple mapping sessions. This methodology was validated in a commercial vineyard, where several universal grids of the crops were generated. Vine rows were correctly reconstructed, although some difficulties appeared around the headland turns as a consequence of unreliable heading estimations. Navigation information conveyed through globally referenced regular grids turned out to be a powerful tool for upcoming practical implementations within agricultural robotics. (C) 2011 Elsevier B.V. All rights reserved.The author would like to thank Juan Jose Pena Suarez and Montano Perez Teruel for their assistance in the preparation of the prototype vehicle, Veronica Saiz Rubio for her help during most of the field experiments, Ratul Banerjee for his contribution in the development of software, and Luis Gil-Orozco Esteve for granting permission to perform multiple tests in the vineyards of his winery Finca Ardal. Gratitude is also extended to the Spanish Ministry of Science and Innovation for funding this research through project AGL2009-11731.Rovira Más, F. (2011). Global-referenced navigation grids for off-road vehicles and environments. Robotics and Autonomous Systems. 60(2):278-287. https://doi.org/10.1016/j.robot.2011.11.007S27828760

Dilemmas and solutions- experiences of a national Family Medicine applied knowledge licensing test during a pandemic

ABSTRACT: Background: The unprecedented COVID-19 pandemic brought significant challenges to all of medicine, including primary care training and examinations. The MRCGP AKT is high-stakes licensing 200-item MCQ for UK trainee family physicians and is part of an assessment tripos that, up to the onset of the pandemic, included a Clinical Skills Assessment using Simulated Patients and workplace based assessment. The AKT is blueprinted onto a curriculum content specification and computer delivered three times a year at test centres across the UK. It tests the knowledge base underpinning independent general practice within the context of the UK National Health Service. We report on the challenges and dilemmas faced during the pandemic, decisions taken, and lessons learned. Rapid exam changes needed to be made, and communicated effectively to candidates, whilst maintaining standards and fairness to candidates. Summary of Work: Challenges included lockdown travel restrictions, reduced capacity, social distancing and shielding candidates being unable to leave home. The April 2020 AKT was cancelled and prioritisation measures implemented to ensure candidates at the end of their training could enter the (stressed) workforce. We engaged with a wide range of stakeholders, carefully looked at remote testing, made contingency plans prioritised for those unable to sit exams and changed exam regulations to ensure fairness to candidates. In this emergency, we delivered a previously published exam which some candidates were unaware they had sat previously, and assessed how these candidates performed. We compared cohort performance before and during the pandemic. Summary of Results: We summarise why we did not remote test, how we obtained key worker status, and adapted contingency plans. Analysis of candidates who had previously sat the same exam showed they performed less well. Despite wide-ranging changes in training and workplace experience, there was no significant difference in cohort performance overall pre-and peri-pandemic. Discussion and Conclusions: COVID-19 constraints changed trainees clinical exposure, restricted training and supervisor support. However, exam preparedness did not appear adversely affected when measured by overall pass rates. Unexpectedly, candidates who sat an identical exam did not benefit from previous exposure. Take-home Messages: Involving stakeholders in key decisions and regular communications are essential. Test security and standards were not compromised

Parameterized Linear Longitudinal Airship Model

A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamic