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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. 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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3. 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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4. 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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5. WEC fault modelling and condition monitoring: A graph‐theoretic approach

   https://doi.org/10.1049/iet-epa.2019.0763  

   Tang, Yufei; Huang, Yu; Lindbeck, Erica; Lizza, Sam; VanZwieten, James; Tom, Nathan; Yao, Wei (April 2020, IET Electric Power Applications)

   The nature of wave resources usually requires wave energy converter (WEC) components to handle peak loads (i.e., torques, forces, and powers) that are many times greater than their average loads, accelerating equipment degradation. Moreover, due to their isolated nature and harsh operating environment, WEC systems are projected to possess high operations and maintenance (O&M) cost, i.e., around 27% of their leveled cost of energy. As such, developing techniques to mitigate these costs through the application of condition monitoring and fault tolerant control will significantly impact the economic feasibility of grid connected WEC power. Toward this goal, models of faulty components are developed in the open source modeling platform, WEC‐Sim, to estimate the performance and measurable states of a WEC operating with likely device and sensor failures. Two types of faulty component models are then applied to a point absorber WEC model with basic controller damping and spring forces. Resulting changes in device behavior are recorded as a benchmark, and a graph‐theoretic approach is proposed for fault detection and identification utilizing multivariate time series. Simulation results demonstrate that these faults can greatly affect the WEC performance, and that the proposed method can effectively detect and classify different types of faults. 

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