AlphaPilot: Autonomous Drone Racing

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to 8m/s and ranked second at the 2019 AlphaPilot Challenge.Comment: Accepted at Robotics: Science and Systems 2020, associated video at https://youtu.be/DGjwm5PZQT

Model based learning for accelerated, limited-view 3D photoacoustic tomography

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data

A 60 yr record of atmospheric carbon monoxide reconstructed from Greenland firn air

We present the first reconstruction of the Northern Hemisphere (NH) high latitude atmospheric carbon monoxide (CO) mole fraction from Greenland firn air. Firn air samples were collected at three deep ice core sites in Greenland (NGRIP in 2001, Summit in 2006 and NEEM in 2008). CO records from the three sites agree well with each other as well as with recent atmospheric measurements, indicating that CO is well preserved in the firn at these sites. CO atmospheric history was reconstructed back to the year 1950 from the measurements using a combination of two forward models of gas transport in firn and an inverse model. The reconstructed history suggests that Arctic CO in 1950 was 140–150 nmol mol-1, which is higher than today's values. CO mole fractions rose by 10–15 nmol mol-1 from 1950 to the 1970s and peaked in the 1970s or early 1980s, followed by a ˜ 30 nmol mol-1 decline to today's levels. We compare the CO history with the atmospheric histories of methane, light hydrocarbons, molecular hydrogen, CO stable isotopes and hydroxyl radicals (OH), as well as with published CO emission inventories and results of a historical run from a chemistry-transport model. We find that the reconstructed Greenland CO history cannot be reconciled with available emission inventories unless unrealistically large changes in OH are assumed. We argue that the available CO emission inventories strongly underestimate historical NH emissions, and fail to capture the emission decline starting in the late 1970s, which was most likely due to reduced emissions from road transportation in North America and Europe

H2 Optimal Coordination of Homogeneous Agents Subject to Limited Information Exchange

Controllers with a diagonal-plus-low-rank structure constitute a scalable class of controllers for multi-agent systems. Previous research has shown that diagonal-plus-low-rank control laws appear as the optimal solution to a class of multi-agent H2 coordination problems, which arise in the control of wind farms. In this paper we show that this result extends to the case where the information exchange between agents is subject to limitations. We also show that the computational effort required to obtain the optimal controller is independent of the number of agents and provide analytical expressions that quantify the usefulness of information exchange