Capturing, classification and concept generation for automated maintenance tasks

Maintenance is an efficient and cost effective way to keep the function of the product available during the product lifecycle. Automating maintenance may drive down costs and improve performance time; however capturing the necessary information required to perform certain maintenance tasks and later building automated platforms to undertake them is very difficult. This paper looks at the creation of a novel methodology tasked with firstly the capture and classification of maintenance tasks and finally conceptual design of platforms for automating maintenance

Maintenance Task Classification: Towards Automated Robotic Maintenance for Industry

AbstractThe business model of high-value capital assets is shifting from purchasing a physical product to acquiring a result or a function supported by the product combined with a number of related services. One such service, maintenance, is perhaps the most efficient way to keep the function available during the product lifecycle. Automation has played a vital role in industry throughout history, particularly within the production line. With the movement towards providing product service systems the need for services such as maintenance are increasingly important for a manufactured product, and the pull towards automation may drive down costs and improve performance time. Although currently robotic applications to maintenance beyond monitoring and inspection tasks are not common, this research aims at exploring the feasibility of future maintenance robots that can perform a variety of maintenance tasks. As its first step, this work looks first at investigation, cataloging and classification of a number of maintenance tasks using standard industrial engineering techniques such as time motion, method or workflow analysis. This involves decomposing the maintenance work into a number of ‘unit tasks’ required to be performed in order to accomplish the specified maintenance

Robotic solutions for sewage systems in coastal urban environments

The objective of this article is to present a specific urban challenge proposed by European cities in ECHORD++ project that can be the starter point of new innovative public procurements. The project that was selected by the project leading team has been a robotic solution for monitoring the sewage system of a European city. Cities are prepared and the Research and Technological Development (RTD) consortia are waiting for real opportunities. The take-off of the robotic technology could be possible: from Lab to Market addressing real urban needs of citizens and cities. The proposals obtained in ECHORD++ could be followed by other cities.Peer ReviewedPostprint (author's final draft

An overview of robotics and autonomous systems for harsh environments

Across a wide range of industries and applications, robotics and autonomous systems can fulfil the crucial and challenging tasks such as inspection, exploration, monitoring, drilling, sampling and mapping in areas of scientific discovery, disaster prevention, human rescue and infrastructure management, etc. However, in many situations, the associated environment is either too dangerous or inaccessible to humans. Hence, a wide range of robots have been developed and deployed to replace or aid humans in these activities. A look at these harsh environment applications of robotics demonstrate the diversity of technologies developed. This paper reviews some key application areas of robotics that involve interactions with harsh environments (such as search and rescue, space exploration, and deep-sea operations), gives an overview of the developed technologies and provides a discussion of the key trends and future directions common to many of these areas

Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Design of a semi-autonomous modular robotic vehicle for gas pipeline inspection

This paper presents a new solution for inspecting and repairing defects in live gas pipelines. The proposed approach is the development of a modular and semi-autonomous vehicle system. The robotic system has a drive mechanism, capable of navigating and adjusting its orientation in various configurations of pipelines. Other features of the system are cable-free communications, semi-autonomous motion control as well as integration of sensory devices. The robotic system is designed to traverse in 150-300 mm diameter pipes through straight and curved sections, junctions and reducers. The vehicle control and navigation technique is implemented using a two-mode controller consisting of a proportional-integral-derivative (PID) and fuzzy logic control. Unlike other available systems, the vehicle employs proprioceptive sensors to monitor its own states. The fuzzy logic controller is used to evaluate the sensor outputs such as speed, climbing angle and rate of change of climbing angle. This control technique allows the vehicle to drive and adapt in a partially observable gas pipe system. Laboratory experiment results are presented. The paper also describes a cable-free communication method for the system. A brief account of typical pipe environments and currently available inspection tools is presented as background information

Design of a semi-autonomous modular robotic vehicle for gas pipeline inspection

This is an article from the journal, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering [© IMechE ]. It is also available at: http://journals.pepublishing.com/content/119778This paper presents a new solution for inspecting and repairing defects in live gas pipelines. The proposed approach is the development of a modular and semi-autonomous vehicle system. The robotic system has a drive mechanism, capable of navigating and adjusting its orientation in various configurations of pipelines. Other features of the system are cable-free communications, semi-autonomous motion control as well as integration of sensory devices. The robotic system is designed to traverse in 150-300 mm diameter pipes through straight and curved sections, junctions and reducers. The vehicle control and navigation technique is implemented using a two-mode controller consisting of a proportional-integral-derivative (PID) and fuzzy logic control. Unlike other available systems, the vehicle employs proprioceptive sensors to monitor its own states. The fuzzy logic controller is used to evaluate the sensor outputs such as speed, climbing angle and rate of change of climbing angle. This control technique allows the vehicle to drive and adapt in a partially observable gas pipe system. Laboratory experiment results are presented. The paper also describes a cable-free communication method for the system. A brief account of typical pipe environments and currently available inspection tools is presented as background information

Virtual Reality via Object Pose Estimation and Active Learning:Realizing Telepresence Robots with Aerial Manipulation Capabilities

This paper presents a novel telepresence system for advancing aerial manipulation indynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot’s workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors, namely, a LiDAR, cameras, and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an active learning pipeline in order to increase the sample efficiency of a pipeline component that relies on a Deep Neural Network (DNN) based object detector. All these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Method-ologically, these results commonly suggest how an awareness of the algorithms’ own failures and uncertainty (“introspection”) can be used to tackle the encountered problems. Moreover, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications

Constrained sampling of 2.5D probabilistic maps for augmented inference

© 2016 IEEE. This work exploits modeling spatial correlation in 2.5D data using Gaussian Processes (GPs), and produces constrained sampling realizations on these models to improve certainty in the predictions by means of integrating additional sparse information. Data organized in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically represented as a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. With the increasing popularity and deployment of robotic devices for infrastructure inspection, 2.5D data becomes a common interpretation of the condition of the target being inspected. Modeling the spatial dependencies and making inferences on new grid locations is a common task that has been addressed using GPs, but inference results on locations which are weakly correlated with the training data are generally not sufficiently informative and distinctly uncertain. The predictive capability of the proposed framework, which is applicable to any 2.5D data, is demonstrated with field inspection data from pipelines. Specifically, sparse and complementary measurements from alternative sensing modalities have been incorporated into the model to predict in more detail local thickness conditions where GP training data is limited. The output of this work aims to probabilistically present variations of the target in the case that both accuracy and reasonable diversity are of significant interest