Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

Conservation Status of Marine Biodiversity in Oceania: An Analysis of Marine Species on the IUCN Red List of Threatened Species

Given the economic and cultural dependence on the marine environment in Oceania and a rapidly expanding human population, many marine species populations are in decline and may be vulnerable to extinction from a number of local and regional threats. IUCN Red List assessments, a widely used system for quantifying threats to species and assessing species extinction risk, have been completed for 1190 marine species in Oceania to date, including all known species of corals, mangroves, seagrasses, sea snakes, marine mammals, sea birds, sea turtles, sharks, and rays present in Oceania, plus all species in five important perciformfish groups. Many of the species in these groups are threatened by themodification or destruction of coastal habitats, overfishing fromdirect or indirect exploitation, pollution, and other ecological or environmental changes associated with climate change. Spatial analyses of threatened species highlight priority areas for both site- and species-specific conservation action. Although increased knowledge and use of newly available IUCN Red List assessments for marine species can greatly improve conservation priorities for marine species in Oceania,many important fish groups are still in urgent need of assessment

Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics

Dynamic tactile exploration enables humans to seamlessly estimate the shape of objects and distinguish them from one another in the complete absence of visual information. Such a blind tactile exploration allows integrating information of the hand pose and contacts on the skin to form a coherent representation of the object shape. A principled way to understand the underlying neural computations of human haptic perception is through normative modelling. We propose a Bayesian perceptual model for recursive integration of noisy proprioceptive hand pose with noisy skin–object contacts. The model simultaneously forms an optimal estimate of the true hand pose and a representation of the explored shape in an object–centred coordinate system. A classification algorithm can, thus, be applied in order to distinguish among different objects solely based on the similarity of their representations. This enables the comparison, in real–time, of the shape of an object identified by human subjects with the shape of the same object predicted by our model using motion capture data. Therefore, our work provides a framework for a principled study of human haptic exploration of complex objects

Experimental evaluation of a passive fuel cell/ battery hybrid power system for an unmanned ground vehicle

Unmanned vehicles are increasing the performance of monitoring and surveillance in several applications. Endurance is a key issue in these systems, in particular in electric vehicles, powered at present mainly by batteries. Hybrid power systems based on batteries and fuel cells have the potential to achieve high energy density and specific energy, increasing also the life time and safe operating conditions of the power system. The objective of this research is to analyze the performance of a passive hybrid power system, designed and developed to be integrated into an existing Unmanned Ground Vehicle (UGV). The proposed solution is based on six LiPo cells, connected in series, and a 200 W PEM fuel cell stack, directly connected in parallel to the battery without any limitation to its charge. The paper presents the characterization of the system behavior, and shows the main results in terms of performance and energy management.The authors would like to acknowledge the NATO Science for Peace and Security Program for partially funding this work through the project “Improving efficiency and operational range in low-power unmanned vehicles through the use of hybrid fuel-cell power systems” (IUFCV), Ref. 985079

A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized

Super-distributed RFID Tag Infrastructures

With the emerging mass production of very small, cheap Radio Frequency Identification (RFID) tags, it is becoming feasible to deploy such tags on a large scale. In this paper, we advocate distribution schemes where passive RFID tags are deployed in vast quantities and in a highly redundant fashion over large areas or object surfaces. We show that such an approach opens up a whole spectrum of possibilities for creating novel RFID-based services and applications, including a new means of cooperation between mobile physical entities. We also discuss a number of challenges related to this approach, such as the density and structure of tag distributions, and tag typing and clustering. Finally, we outline two prototypical applications (a smart autonomous vacuum cleaner and a collaborative map-making system) and indicate future directions of research

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

Conservation Status of Marine Biodiversity in Oceania: An Analysis of Marine Species on the IUCN Red List of Threatened Species

Given the economic and cultural dependence on the marine environment in Oceania and a rapidly expanding human population, many marine species populations are in decline and may be vulnerable to extinction from a number of local and regional threats. IUCN Red List assessments, a widely used system for quantifying threats to species and assessing species extinction risk, have been completed for 1190 marine species in Oceania to date, including all known species of corals, mangroves, seagrasses, sea snakes, marine mammals, sea birds, sea turtles, sharks, and rays present in Oceania, plus all species in five important perciform fish groups. Many of the species in these groups are threatened by the modification or destruction of coastal habitats, overfishing from direct or indirect exploitation, pollution, and other ecological or environmental changes associated with climate change. Spatial analyses of threatened species highlight priority areas for both site- and species-specific conservation action. Although increased knowledge and use of newly available IUCN Red List assessments for marine species can greatly improve conservation priorities for marine species in Oceania, many important fish groups are still in urgent need of assessment

Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps

International audienceLidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator