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1. A Deep Learning Approach to Localization for Navigation on a Miniature Autonomous Blimp

   https://doi.org/10.1109/ICCA51439.2020.9264514  

   Seguin, Landan ; Zheng, Justin ; Li, Alberto ; Tao, Qiuyang ; Zhang, Fumin ( October 2020 , IEEE International Conference on Control and Automation (ICCA))

   null (Ed.)

   The Georgia Tech Miniature Autonomous Blimp (GT-MAB) needs localization algorithms to navigate to way-points in an indoor environment without leveraging an external motion capture system. Indoor aerial robots often require a motion capture system for localization or employ simultaneous localization and mapping (SLAM) algorithms for navigation. The proposed strategy for GT-MAB localization can be accomplished using lightweight sensors on a weight-constrained platform like the GT-MAB. We train an end-to-end convolutional neural network (CNN) that predicts the horizontal position and heading of the GT-MAB using video collected by an onboard monocular RGB camera. On the other hand, the height of the GT-MAB is estimated from measurements through a time-of-flight (ToF) single-beam laser sensor. The monocular camera and the single-beam laser sensor are sufficient for the localization algorithm to localize the GT-MAB in real time, achieving the averaged 3D positioning errors to be less than 20 cm, and the averaged heading errors to be less than 3 degrees. With the accuracy of our proposed localization method, we are able to use simple proportional-integral-derivative controllers to control the GT-MAB for waypoint navigation. Experimental results on the waypoint following are provided, which demonstrates the use of a CNN as the primary localization method for estimating the pose of an indoor robot that successfully enables navigation to specified waypoints. 

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2. Better Together: Online Probabilistic Clique Change Detection in 3D Landmark-Based Maps

   Bateman, Sam ; Harlow, Kyle ; Heckman, Christoffer ( October 2020 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment. 

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3. Wind Profiling in the Lower Atmosphere from Wind-Induced Perturbations to Multirotor UAS

   https://doi.org/10.3390/s20051341  

   González-Rocha, Javier ; De Wekker, Stephan F. ; Ross, Shane D. ; Woolsey, Craig A. ( March 2020 , Sensors)

   We present a model-based approach to estimate the vertical profile of horizontal wind velocity components using motion perturbations of a multirotor unmanned aircraft system (UAS) in both hovering and steady ascending flight. The state estimation framework employed for wind estimation was adapted to a set of closed-loop rigid body models identified for an off-the-shelf quadrotor. The quadrotor models used for wind estimation were characterized for hovering and steady ascending flight conditions ranging between 0 and 2 m/s. The closed-loop models were obtained using system identification algorithms to determine model structures and estimate model parameters. The wind measurement method was validated experimentally above the Virginia Tech Kentland Experimental Aircraft Systems Laboratory by comparing quadrotor and independent sensor measurements from a sonic anemometer and two SoDAR instruments. Comparison results demonstrated quadrotor wind estimation in close agreement with the independent wind velocity measurements. However, horizontal wind velocity profiles were difficult to validate using time-synchronized SoDAR measurements. Analysis of the noise intensity and signal-to-noise ratio of the SoDARs proved that close-proximity quadrotor operations can corrupt wind measurement from SoDARs, which has not previously been reported. 

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4. Low-cost camera and 2-D LIDAR fusion for target vehicle corner detection and tracking: Applications to micromobility devices

   https://doi.org/10.1016/j.ymssp.2023.110891  

   Alai, Hamidreza ; Rajamani, Rajesh ( January 2024 , Mechanical Systems and Signal Processing)

   This paper develops a cost-effective vehicle detection and tracking system based on fusion of a 2-D LIDAR and a monocular camera to protect electric micromobility devices, especially e-scooters, by predicting the real- time danger of a car- scooter collision. The cost and size disadvantages of 3-D LIDAR sensors make them an unsuitable choice for micromobility devices. Therefore, a 2-D RPLIDAR Mapper sensor is used. Although low-cost, this sensor comes with major shortcomings such as the narrow vertical field of view and its low density of data points. Due to these factors, the sensor does not have a robust output in outdoor applications, and the measurements keep jumping and sliding on the vehicle surface. To improve the performance of the LIDAR, a single monocular camera is fused with the LIDAR data not only to detect vehicles, but also to separately detect the front and side of a target vehicle and to find its corner. It is shown that this corner detection method is more accurate than strategies that are only based on the LIDAR data. The corner measurements are used in a high-gain observer to estimate the location, velocity, and orientation of the target vehicle. The developed system is implemented on a Ninebot e-scooter platform, and multiple experiments are performed to evaluate the performance of the algorithm. 

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5. Coordinated failure response and recovery in a decentralized microgrid architecture

   https://doi.org/10.1109/ECCE.2017.8096819  

   Bani-Ahmed, Abedalsalam ; Rashidi, Mohammad ; Nasiri, Adel ( October 2017 , 2017 IEEE Energy Conversion Congress and Exposition (ECCE))

   The challenge of failure management emerges in decentralized architectures due to the distributed nature of the layered control process. Failures in microgrid system may occur at the microgrid level or any of the control layers. A failure detection and response mechanism is required to attain a reliable, fault-tolerant microgrid operation. This paper introduces a Failure Management Unit as an essential function in a microgrid Energy Management System. The proposed unit comprises failure detection mechanisms and a recovery algorithm. The proposed system is applied to a microgrid case study and shows a robust detection and recovery outcome during a system failure. The real-time experimental results were achieved using Hardware-In-the-Loop platform. Coordination between controllers during the recovery period requires low-bandwidth communications, which has no significant overhead on the communication infrastructure. 

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