Climbing Robots’ Mobility for Inspection and Maintenance of 3D Complex Environments

For complex climbing robots, which work in difficult 3D outdoor environments, the gravity force has an important influence with respect the robots changes during its motion. This type of climbing robots is self-supported in the complex 3D structures (bridges, skeleton of the buildings, etc.) which require periodic, manually performed inspections and maintenance. The use of non-conventional climbing robots for this type of operation is highly appropriate. Their locomotion system commonly comprises arms/legs that permit the robots 3D mobility (gait). These mechanisms also enable the robot to support itself and guarantee its stability. This paper presents the main features of non-conventional climbing robots mobility on complex 3D environments: power supply, number of DOFs, lightweight structure, gait, speed, secure grasp, etc. It also covers the general theory underlying the design of climbing robots, their kinematics, with its specific, unconventional mobility. The paper not only describes the climbing robot mobility theory but also provides several examples taken from the ROMA and MATS robots families. The developed robots have high degree of autonomy with totally on-board control system. These autonomous robots demonstrate in the course of real experimentation that the criteria for design, control strategy and path planning are accurate. Finally, the paper examines trends in climbing robot technology.Carlos Balaguer received his Ph.D. in Automation from the Polytechnic University of Madrid (UPM), Spain in 1983. From 1983–1994 he was with the Department of Systems Engineering and Automation of the UPM as Associated Professor. Since 1994, he has been a Full Professor of the Robotics Lab at the University Carlos III of Madrid. Prof. Balaguers research has included robot design and development, robot control, path & task planning, force-torque control, assistive and service robots, climbing robots, legged and humanoid robots, and human-robot interaction. He has published more than 120 papers in journals and conference proceedings, and several books in the field of robotics. He is a member of IEEE and IFAC, and former President of IAARC.Antonio Gimenez studied Electrical Engineering at the Polytechnic University of Madrid and received his PhD from the University Carlos III of Madrid in 2000. Currently he is Associated Professor at the Robotics Lab atthe University Carlos III of Madrid. He participated in numerous national and international R&D projects in robotics and automation. His research interest includes design and robot development, rehabilitation robots, climbing robots, and automation in construction. Recently he is very active in the field of computer-aided mechatronics design. He has published numerous refereed publications in international journals, and conference proceedings.Alberto Jardón Huete is currently finishing his Ph.D. degree in Automation Engineering. He received his B.Sc. in electronics engineering (1998) and is graduated in Electrical Engineering (2002) at University Carlos III of Madrid. He is an active member of the Robotics Lab since 1997, and has collaborated in the development of the climbing robots ROMA I, ROMA II, and other research projects of relevance. Currently he is focused in the design and development of light weight service robots. His interests include assistive robotic design, mechatronics, robotic research, the development of tools to perform this research and the transfer of robotics technology to industry.     

From eager to lazy constrained data acquisition: A general framework

^*1 Constraint Satisfaction Problems (CSPs)¹⁷⁾ are an effective framework for modeling a variety of real life applications and many techniques have been proposed for solving them efficiently. CSPs are based on the assumption that all constrained data (values in variable domains) are available at the beginning of the computation. However, many non-toy problems derive their parameters from an external environment. Data retrieval can be a hard task, because data can come from a third-party system that has to convert information encoded with signals (derived from sensors) into symbolic information (exploitable by a CSP solver). Also, data can be provided by the user or have to be queried to a database. For this purpose, we introduce an extension of the widely used CSP model, called Interactive Constraint Satisfaction Problem (ICSP) model. The variable domain values can be acquired when needed during the resolution process by means of Interactive Constraints, which retrieve (possibly consistent) information. A general framework for constraint propagation algorithms is proposed which is parametric in the number of acquisitions performed at each step. Experimental results show the effectiveness of the proposed approach. Some applications which can benefit from the proposed solution are also discussed. This paper is an extended and revised version of the paper presented at IJCAI’99 (Stockholm, August 1999)⁴⁾. Paola Mello, Ph.D.: She received her degree in Electronic Engineering from University of Bologna, Italy, in 1982 and her Ph.D. degree in Computer Science in 1989. Since 1994 she is full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna where she teaches Artificial Intelligence. Her research activity focuses around: programming languages, with particular reference to logic languages and their extensions towards modular and object-oriented programming; artificial intelligence; knowledge representation; expert systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national (Progetti Finalizzati e MURST) and international (UE) research projects in the context of computational logic. Michela Milano, Ph.D.: She is a Researcher in the Department of Electronics, Computer Science and Systems at the University of Bologna. From the same University she obtained her master degree in 1994 and her Ph.D. in 1998. In 1999 she had a post-doc position at the University of Ferrara. Her research focuses on Artificial Intelligence, Constraint Satisfaction and Constraint Programming. In particular, she worked on using and extending the constraint-based paradigm for solving real-life problems such as scheduling, routing, object recognition and planning. She has served on the program committees of several international conferences in the area of Constraint Satisfaction and Programming, and she has served as referee in several related international journals. Marco Gavanelli: He is currently a Ph.D. Student in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. His research interest include Artificial Intelligence, Constraint Logic Programming, Constraint Satisfaction and visual recognition. He is a member of ALP (the Association for Logic Programming) and AI^*IA (the Italian Association for Artificial Intelligence). Evelina Lamma, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on logic programming languages, Artificial Intelligence and software engineering. She was co-organizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. She is a member of the Executive Committee of the Italian Association for Artificial Intelligence (AI^*IA). Currently, she is Full Professor at the University of Ferrara, where she teaches Artificial Intelligence and Fondations of Computer Science. Massimo Piccardi, Ph.D.: He graduated in electronic engineering at the University of Bologna, Italy, in 1991, where he received a Ph.D. in computer science and computer engineering in 1995. He currently an assistant professor of computer science with the Faculty of Engineering at the University of Ferrara, Italy, where he teaches courses on computer architecture and microprocessor systems. Massimo Piccardi participated in several research projects in the area of computer vision and pattern recognition. His research interests include architectures, algorithms and benchmarks for computer vision and pattern recognition. He is author of more than forty papers on international scientific journals and conference proceedings. Dr. Piccardi is a member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter. Rita Cucchiara, Ph.D.: She is an associate professor of computer science at the Faculty of Engineering at the University of Modena and Reggio Emilia, Italy, where she teaches courses on computer architecture and computer vision. She graduated in electronic engineering at the University of Bologna, Italy, in 1989 and she received a Ph.D. in electronic engineering and computer science from the same university in 1993. From 1993 to 1998 she been an assistant professor of computer science with the University of Ferrara, Italy. She participated in many research projects, including a SIMD parallel system for vision in the context of an Italian advanced research program in robotics, funded by CNR (the Italian National Research Council). Her research interests include architecture and algorithms for computer vision and multimedia systems. She is author of several papers on scientific journals and conference proceedings. She is member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter.     

A platform for virtual collaboration spaces and educational communities: the case of EVE

This paper presents the design, implementation and evaluation of EVE Community Prototype, which is an educational virtual community aiming to meet the requirements of a Virtual Collaboration Space and to support e-learning services. Furthermore, this paper describes the design and implementation of an integrated platform for Networked Virtual Environments, called EVE Platform, which supports the afore-mentioned educational community. This platform supports stable event sharing and creation of multi-user three dimensional (3D) places, H.323-based voice over IP services integrated in 3D spaces as well as multiple concurrent virtual worlds. Christos Bouras obtained his Diploma and PhD from the Department Of Computer Engineering and Informatics of Patras University (Greece). He is currently an Associate Professor in the above department. Also he is a scientific advisor of Research Unit 6 in Research Academic Computer Technology Institute (CTI), Patras, Greece. His research interests include Analysis of Performance of Networking and Computer Systems, Computer Networks and Protocols, Telematics and New Services, QoS and Pricing for Networks and Services, e-Learning Networked Virtual Environments and WWW Issues. He has extended professional experience in Design and Analysis of Networks, Protocols, Telematics and New Services. He has published 200 papers in various well-known refereed conferences and journals. He is a co-author of seven books in Greek. He has been a PC member and referee in various international journals and conferences. He has participated in R&D projects such as RACE, ESPRIT, TELEMATICS, EDUCATIONAL MULTIMEDIA, ISPO, EMPLOYMENT, ADAPT, STRIDE, EUROFORM, IST, GROWTH and others. Also he is member of experts in the Greek Research and Technology Network (GRNET), Advisory Committee Member to the World Wide Web Consortium (W3C), Member of WG3.3 and WG6.4 of IFIP, Task Force for Broadband Access in Greece, ACM, IEEE, EDEN, AACE and New York Academy of Sciences. Eleftheria Giannaka obtained her Diploma from the Informatics Department of the Aristotelian University of Thessaloniki (Greece) and her Masters Degree from the Computer Engineering and Informatics Department of Patras University. She is currently a PhD Candidate of the Department of Computer Engineer and Informatics of Patras University. Furthermore, she is working as an R&D Computer Engineer at the Research Unit 6 of the Computer Technology Institute in Patra (Greece). Her interests include Computer Networks, Virtual Networks, System Architecture, Internet Applications, Electronic Commerce, Database Implementation and Administration, Virtual Reality applications, Performance Evaluation and Programming. Alexandros Panagopoulos was born in Pyrgos, Greece, 1981. He obtained his Diploma, from the Computer Engineering and Informatics Department of Patras University (Greece). In 2000 he became a member of Research Unit 6 of the Computer Technology Institute (CTI). His interests include Computer Networks, Multiuser Virtual Environments, Telematics, and C/C++ and Java programming. Dr. Thrasyvoulos Tsiatsos obtained his Diploma, his Master's Degree and his PhD from the Computer Engineering and Informatics Department of Patras University (Greece). He is currently an R&D Computer Engineer at the Research Unit 6 of Computer Technology Institute, Patras, Greece. His research interests include Computer Networks, Telematics, Distributed Systems, Networked Virtual Environments, Multimedia and Hypermedia. More particular he is engaged in Distant Education with the use of Computer Networks, Real Time Protocols and Networked Virtual Environments. He has published nine papers in journals and 30 papers in well-known refereed conferences. He has participated in R&D projects such as OSYDD, RTS-GUNET, ODL-UP, VES, ODL-OTE, INVITE, VirRAD and EdComNet.     

Robust detection and ordering of ellipses on a calibration pattern

The aim of this work is to accurately estimate from an image the parameters of some ellipses and their relative positions with respect to a given pattern. The process is characterized because it is fully automated and is robust against image noise and occlusions. We have built a calibrator pattern with two planes each containing several ordered circles in known 3D positions. Our method is able to estimate the position of every ellipse and to put them into correspondence with the original calibrator circles. The text was submitted by the authors in English. Luis álvarez received an MS in applied mathematics in 1985 and a PhD in mathematics in 1988, both from CompIntense University (Madrid, Spain). Between 1991 and 1992, he worked as a postdoctoral researcher at CEREMADE, Université Paris IX—Dauphine (France). Currently, he is with the Computer Science Department of the University of Las Palmas de Gran Canaria. His research interests are computer vision and partial differential equations. He is the scientific leader of computer vision group of the University of Las Palmas named AMI. Agustín Salgado received an MS in computer science in from the University of Las Palmas de Gran Canaria (Las Palmas, Spain). Currently, he holds a grant from the Computer Science Department of the University of Las Palmas de Gran Canaria, where he is working on his doctoral thesis under the direction of Javier Sánchez. Javier Sánchez received an MS in computer science in 1997 and a PhD in computer science in 2001, both from the University of Las Palmas de Gran Canaria (Las Palmas, Spain). Between 1997 and 1998, he attended some courses of the DBA 127 “Informatique: Systemes Intelligentes” at the Université Paris IX—Dauphine (France). Currently, he is a lecturer at the Computer Science Department of the University of Las Palmas de Gran Canaria. His research interests are computer vision and partial differential equations, specially applied to stereoscopic vision and optical flow estimation.     

Efficient minimum spanning tree algorithms on the reconfigurable mesh

1 IntroductionLet G = (V, E) be a connected, undirected graph with a weight function W on the set Eof edges to the set of reals. A spanning tree is a subgraph T = (V, ET), ET G E, of C suchthat T is a tree. The weight W(T) of a spanning tree T is the sum of the weights of its edges.A spanning tree with the smallest possible'weight is called a minimum spanning tree (MST)of G. Computing an MST of a given weighted graph is an important problem that arisesin many applications. For this …     

Multiresolution volumetric 3D object reconstruction for collaborative interactions

Within the framework of collaborative interactions with 3D numerical copies of real objects inserted in virtual environments, this paper tackles the issue of 3D object reconstruction from multiple calibrated cameras. After examining the various constraints related to collaborative systems, we propose comprehensive, state of the art 3D reconstruction techniques. The main families of approaches are here identified, described, and discussed in detail. An analysis of the literature shows that there is a lack of methods that are able to respond to the needs of the collaborative interaction applications, and that perform adequately in terms of computation time and reconstruction accuracy. Accordingly, we propose a new multiresolution volumetric approach that is able to obtain numerical copies of real objects at multiple resolutions. Experimental results demonstrate that the proposed approach provides accurate reconstructions at reasonable, interactive computation times. The use of the proposed approach for progressive insertion of reconstructed objects in the prototype interfaces MOWGLI and Spin-3D developed by FranceTelecom R&D is also illustrated. The text was submitted by the authors in English. Rachid Guerchouche. Was born in Algeria in 1979. He received an Engineer Degree in electronics from the National Polytechnic School of Algiers, Algeria in June 2004, and a research Masters Degree in Virtual Reality and Complex Systems Engineering from Université Evry-Val d’Essonne (France) in June 2005. He is currently a PhD student jointly in the ARTEMIS Department at TELECOM & Management SudParis and in the VIA Project Unit of the IRIS Laboratory at France Télécom R&D. Olivier Bernier. Was born in 1964. He received the diploma of the Ecole Polytech-nique (Palaiseau, France) in 1986 and the diploma of the Ecole Nationale Supérieure des Télécommunications (Paris, France) in 1988. Since this date, he has worked as a Research Engineer at Orange Labs-FT R&D, the research center of France Telecom. His areas of interest are computer vision, statistical learning and pattern recognition, in particular applied to advanced human machine interfaces. Titus Zaharia. Received an Engineer Degree in Electronics and the Masters Degree in Electronics from University POLITEHNICA (Bucharest, Romania) in 1995 and 1996, respectively. In 2001, he obtained a PhD degree in Mathematics and Computer Science from University Paris V—René Descartes (Paris, France). He then joined the ARTEMIS Department at TELECOM & Management SudParis as a research engineer, and became an Associate Professor in 2002. His research interests concern visual content indexing and coding, and include feature extraction, image and video segmentation, motion detection and estimation, 2D/3D reconstruction, virtual character modelling and animation, virtual/augmented reality, digital interactive TV, calibration techniques, and color image processing. Titus Zaharia is a member of SPIE.     

Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018

ABSTRACT

To support shifting to high mix/low volume production, manufacturers in high wage countries aim for robotizing their production operations – with a special focus on the late production phases, where robotic assembly cells are then confronted with any complexities resulting from part and product varieties. The ‘World Robot Challenge 2018’ (WRC 2018) emulated such high mix/low volume production scenarios in a competition taking place in Tokyo, Japan. As part of our activities in SDU's newly founded I4.0 Lab, we integrated and advanced our experiences and developments from our various R & D projects in a novel robotic assembly cell design to compete in the WRC 2018. This article describes the system architecture as well as main aspects of its implementation regarding robot control, robot programming and computer vision and how they contributed to winning the challenge. Due to the application of collaborative robots, the cell design allows for operation without fences. Hence, multiple copies of the cell can be arranged in a highly reconfigurable, highly adaptable matrix structure in which several production flows can be handled concurrently. This concept was demonstrated by the installation of a duplicate cell that allowed for parallel developments on two cells and prolonged development also after shipping the first cell to Japan.     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.