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Laser trackers have emerged as a powerful tool for accurate and precise measurement in a variety of industries, including hydro. We provide a comprehensive review of the current trends and future prospects of laser tracker applications in the hydro industry in this research paper. We begin by discussing the fundamentals of laser trackers, such as their operating principles and key features. We then look at the various applications of laser trackers in the hydro industry, such as dam monitoring, turbine alignment, asbuilt verification, and hydro component inspection. We examine the most recent advancements and innovations in laser tracker technology, such as increased accuracy, portability, and integration with other measurement tools. In addition, we discuss the advantages and disadvantages of using laser trackers in the hydro industry, such as increased efficiency, reduced downtime, and improved safety, as well as limitations such as environmental conditions, line of sight, and cost considerations. Finally, we talk about the future of laser tracker applications in the hydro industry, including potential growth areas like digital twin technology, augmented reality, and automation. We conclude with research recommendations and emphasis the importance of laser trackers as a valuable tool for improving measurement accuracy and efficiency in the hydro industry.

Low-fidelity analytical models of turbine wakes have traditionally been used for wind farm planning, performance evaluation, and demonstrating the utility of advanced control algorithms in increasing the annual energy production. In practice, however, it remains challenging to correctly estimate the flow and achieve significant performance gains using conventional low-fidelity models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the effects of atmospheric boundary layer turbulence and the complex aerodynamic interactions among wind turbines. To improve the predictive capability of low-fidelity models while remaining amenable to control design, we offer a stochastic dynamical modeling framework for capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. In this approach, we use stochastically forced linear models of the turbulent velocity field to augment the analytically computed wake velocity and achieve consistency with higher-fidelity models in capturing power and thrust force measurements. The power-spectral densities of our stochastic models are identified via convex optimization to ensure consistency with partially available velocity statistics or power and thrust force measurements while preserving model parsimony. We demonstrate the utility of our approach in capturing turbulence intensity variations behind wind turbines and estimating thrust force and power signals generated by large-eddy simulations of the flow over a cascade of turbines. Our results provide insight into the significance of sparse field measurements in recovering the statistical signature of wind farm turbulence using stochastic linear models., Comment: 21 pages, 11 figures

This file contains the Appendix accompanying the paper "Surprisingly Robust In-Hand Manipulation: An Empirical Study" This Appendix accompanies the paper "Surprisingly Robust In-Hand Manipulation: An Empirical Study" [1]. Abstract: We present in-hand manipulation skills on a dexterous, compliant, anthropomorphic hand. Even though these skills were derived in a simplistic manner, they exhibit surprising robustness to variations in shape, size, weight, and placement of the manipulated object. They are also very insensitive to variation of execution speeds, ranging from highly dynamic to quasi-static.The robustness of the skills leads to compositional properties that enable extended and robust manipulation programs. To explain the surprising robustness of the in-hand manipulation skills, we performed a detailed, empirical analysis of the skills’ performance. From this analysis, we identify three principles for skill design: 1) Exploiting the hardware’s innate abilityto drive hard-to-model contact dynamics. 2) Taking actions to constrain these interactions, funneling the system into a narrow set of possibilities. 3) Composing such action sequences into complex manipulation programs. We believe that these principles constitute an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general. [1] Aditya Bhatt, Adrian Sieler, Steffen Puhlmann and Oliver Brock. "Surprisingly Robust In-Hand Manipulation: An Empirical Study," in Proceedings of Robotics: Science and Systems, 2021.

Off-policy temporal difference (TD) methods are a powerful class of reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD algorithms are not commonly used in combination with feature normalization techniques, despite positive effects of normalization in other domains. We show that naive application of existing normalization techniques is indeed not effective, but that well-designed normalization improves optimization stability and removes the necessity of target networks. In particular, we introduce a normalization based on a mixture of on- and off-policy transitions, which we call cross-normalization. It can be regarded as an extension of batch normalization that re-centers data for two different distributions, as present in off-policy learning. Applied to DDPG and TD3, cross-normalization improves over the state of the art across a range of MuJoCo benchmark tasks.