Miguel Castano

Bio: Miguel Castano is an academic researcher from Luleå University of Technology. The author has contributed to research in topics: Process control & Asset management. The author has an hindex of 5, co-authored 14 publications receiving 83 citations. Previous affiliations of Miguel Castano include Katholieke Universiteit Leuven.

Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection

Abstract: This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu’s threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.

Cloud computing for big data analytics in the Process Control Industry

Abstract: The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0.

Uncertainty bounds for gramian-based interaction measures

Abstract: Bounds of two Gramian-based Interaction Measures (IM:s) induced by model uncertainty are presented in this paper. The connection between the considered IM:s (the Hankel Interaction Index Array (HIIA) and the Participation Matrix (PM)) is explored, showing that it is possible in certain cases to translate the bounds of one into bounds of the other. The first method is a tightening of previously suggested uncertainty bounds for the HIIA. The second method is based on a novel exploration of the relationship between the PM and the area enclosed by the Nyquist diagram. The latter method is a numerical approximation of the analytical bounds of the PM, whilst the former one provides very loose bounds for the examples presented here.

Model based and empirical approaches to robust control structure selection based on the H2-norm

Abstract: This paper presents a method for the robust control structure selection based on the assessment of the H2-norm. For both uncertain parametric models and non parametric estimated frequency response ...

CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

Abstract: Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy hard to achieve. This has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, the CERBERUS system-of-systems is presented as a unified strategy towards subterranean exploration using legged and flying robots. As primary robots, ANYmal quadruped systems are deployed considering their endurance and potential to traverse challenging terrain. For aerial robots, both conventional and collision-tolerant multirotors are utilized to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, a complementary multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data for resilient robot pose estimation is proposed. Individual robot pose estimates are refined by a centralized multi-robot map optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined ∗1 Robotic Systems Lab, ETH Zurich, 2 Autonomous Robots Lab, University of Nevada, Reno — Norwegian University of Science and Technology, 3 Autonomous Systems Lab, ETH Zurich, 4 Flyability, 5 Oxford Robotics Institute, University of Oxford 6 University of California, Berkeley, 7 Sierra Nevada Corporation, Direct correspondence to Marco Tranzatto marcot@ethz.ch ar X iv :2 20 1. 07 06 7v 1 [ cs .R O ] 1 8 Ja n 20 22 coordinate frame. Furthermore, a unified exploration path planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication between the robots and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station, alongside breadcrumbing of wireless nodes by our legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge Tunnel and Urban Circuits, along with the current limitations and the lessons learned for the benefit of the community.

The area enclosed by the (oriented) Nyquist Diagram and the Hilbert- Schmidt-Hankel norm of a linear system

Abstract: It is shown that the Hilbert-Schmidt-Hankel norm (HSH-norm) of a transfer function of a stable system is equal, up to a constant factor, to the square root of the area enclosed by the oriented Nyquist diagram of the transfer function (multiplicities included). A generalization is presented for the case of systems which have no poles on the stability boundary, but otherwise have no restrictions on the pole locations. >

Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies

Abstract: In the past, industrial control of field devices was comprised of self-contained systems in a dedicated network for exchanging control information between field devices and control hardware to accomplish process tasks. Nowadays, cloud computing enables a massive amount of computing resources and high availability, which opens up new potentials in the industrial sector. Until now, the integration of cloud solutions in industrial control was limited due to missing technologies connecting the Internet of Things with industrial requirements. Furthermore, based on existing paradigms there is a lack of appropriate architecture concepts for industrial control. This paper depicts a platform concept, which combines cloud computing and industrial control using edge devices realized for an automation cell.