A Robust Localization System for Inspection Robots in Sewer Networks †

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2

Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms.

This cumulative thesis introduces a novel field for service robotics: the inspection of marine vessels using mobile inspection robots. In this thesis, three scientific contributions are provided and experimentally verified in the field of marine inspection, but are not limited to this type of application. The inspection scenario is merely a golden thread to combine the cumulative scientific results presented in this thesis. The first contribution is an adaptive, proprioceptive control approach for hybrid leg-wheel robots, such as the robot ASGUARD described in this thesis. The robot is able to deal with rough terrain and stairs, due to the control concept introduced in this thesis. The proposed system is a suitable platform to move inside the cargo holds of bulk carriers and to deliver visual data from inside the hold. Additionally, the proposed system also has stair climbing abilities, allowing the system to move between different decks. The robot adapts its gait pattern dynamically based on proprioceptive data received from the joint motors and based on the pitch and tilt angle of the robot's body during locomotion. The second major contribution of the thesis is an independent ship inspection system, consisting of a magnetic wall climbing robot for bulkhead inspection, a particle filter based localization method, and a spatial content management system (SCMS) for spatial inspection data representation and organization. The system described in this work was evaluated in several laboratory experiments and field trials on two different marine vessels in close collaboration with ship surveyors. The third scientific contribution of the thesis is a novel approach to structural classification using semantic perception approaches. By these methods, a structured environment can be semantically annotated, based on the spatial relationships between spatial entities and spatial features. This method was verified in the domain of indoor perception (logistics and household environment), for soil sample classification, and for the classification of the structural parts of a marine vessel. The proposed method allows the description of the structural parts of a cargo hold in order to localize the inspection robot or any detected damage. The algorithms proposed in this thesis are based on unorganized 3D point clouds, generated by a LIDAR within a ship's cargo hold. Two different semantic perception methods are proposed in this thesis. One approach is based on probabilistic constraint networks; the second approach is based on Fuzzy Description Logic and spatial reasoning using a spatial ontology about the environment

A survey on multi-robot coverage path planning for model reconstruction and mapping

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives

Development of a Wall Climbing Robot and Ground Penetrating Radar System for NonDestructive Testing of Vertical Safety Critical Concrete Structures

This research aims to develop a unique adhesion mechanism for wall climbing robot to automate the technology of non-destructive testing (NDT) of large safety critical reinforced concrete structures such as nuclear power plants, bridge columns, dams etc. This research work investigates the effect of key design parameters involved in optimizing the adhesion force achieved from rare earth neodymium magnets. In order to penetrate a nominal concrete cover to achieve magnetic coupling with buried rebar and generate high enough adhesion force by using minimum number of permanent magnets, criteria such as distance between multiple magnets, thickness of flux concentrator are evaluated by implementing finite element analysis (FEA). The proposed adhesion module consists of three N42 grade neodymium magnets arranged in a unique arrangement on a flux concentrator called yoke. The preliminary FEA results suggest that, using two yoke modules with minimum distance between them generate 82 N higher adhesion force compared to a single module system with higher forceto-weight ratio of 4.36. Presence of multiple rebars in a dense mesh setting can assist the adhesion module to concentrate the magnetic flux along separate rebars. This extended concentration area has led to higher adhesion force of 135.73 N as well as enabling the robot to take turns. Results suggest that, having a 50×50 mm rebar meshing can sustain steep robot rotational movement along it’s centre of gravity where the adhesion force can fall as low as 150 N. A small, mobile prototype robot with on-board force sensor is built that exhibited 3600 of manoeuvrability on a 50×50 mm meshed rebars test rig with maximum adhesion force of 108 N at 35 mm air gap. Both experiment and simulationresults prove that the magnetic adhesion mechanism can generate efficient adhesion force for the climbing robot to operate on vertical reinforced concrete structures. In terms of the NDT sensor, an in-depth analysis of the ground penetrating radar (GPR) is carried out to develop a low cost operational laboratory prototype. A one-dimensional numerical framework based on finite difference time domain (FDTD) method is developed to model response behaviour of a GPR. The effects of electrical properties such as dielectric constant, conductivity of the media are evaluated. A Gaussian shaped pulse is used as source which propagates through the 1D array grid, and the pulse interactions at different media interfaces are investigated. A real life application of GPR to detect a buried steel bar in 1 m thick concrete block is modelled, and the results present 100% accurate detection of the steel bar along with measured depth of the concrete cover. The developed framework could be implemented to model multi-layer dielectric blocks with detection capability of various buried objects. Experimental models are built by utilizing a proposed antenna miniaturization technique of dipole antenna with additional radiating arms. The resultant reflection coefficient values indicate a reduction of 55% and 44% in length reduction compared to a conventional 100 MHz and 200 MHz dipole antenna respectively. The GPR transmitting pulse generator features an enhanced tuneable feature to make the GPR system more adaptable to various environmental conditions. The prototype pulse generator circuit can produce pulses with variable width from 750 ps to 10 ns. The final assembled robotic GPR system’s performance is validated by its capability of detecting and localizing an aluminium sheet and a rebar of 12 mm diameter buried under a test rig built of wood to mimic the concrete structure environment. The final calculations reveal a depth error of +0.1 m. However, the key focus of this work is to prove the design concept and the error in measurement can be addressed by utilizing narrower bandwidth pulse that the proposed pulse generator is capable of generating. In general, the proposed robotic GPR system developed in this research proves the concept of feasibility of undertaking inspection procedure on large concrete structures in hazardous environments that may not be accessible to human inspector

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements