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2161 Acknowledgements Acknowl. B.21 Actuators for Soft Robotics F.58 Robotics in Hazardous Applications by Alin Albu-Schäffer, Antonio Bicchi by James Trevelyan, William Hamel, The authors of this chapter have used liberally of Sung-Chul Kang work done by a group of collaborators involved James Trevelyan acknowledges Surya Singh for de- in the EU projects PHRIENDS, VIACTORS, and tailed suggestions on the original draft, and would also SAPHARI. We want to particularly thank Etienne Bur- like to thank the many unnamed mine clearance experts det, Federico Carpi, Manuel Catalano, Manolo Gara- who have provided guidance and comments over many bini, Giorgio Grioli, Sami Haddadin, Dominic Lacatos, years, as well as Prof. S. Hirose, Scanjack, Way In- Can zparpucu, Florian Petit, Joshua Schultz, Nikos dustry, Japan Atomic Energy Agency, and Total Marine Tsagarakis, Bram Vanderborght, and Sebastian Wolf for Systems for providing photographs. their substantial contributions to this chapter and the William R. Hamel would like to acknowledge work behind it. the US Department of Energy’s Robotics Crosscut- ting Program and all of his colleagues at the na- C.29 Inertial Sensing, GPS and Odometry tional laboratories and universities for many years by Gregory Dudek, Michael Jenkin of dealing with remote hazardous operations, and all We would like to thank Sarah Jenkin for her help with of his collaborators at the Field Robotics Center at the figures. Carnegie Mellon University, particularly James Os- born, who were pivotal in developing ideas for future D.36 Motion for Manipulation Tasks telerobots. by James Kuffner, Jing Xiao Sungchul Kang acknowledges Changhyun Cho, We acknowledge the contribution that the authors of the Woosub Lee, Dongsuk Ryu at KIST (Korean Institute first edition made to this chapter revision, particularly for Science and Technology), Korea for their provid- Sect. 36.1, Sect. 36.2, and Sect. 36.5. ing valuable documents and pictures. He also appreci- ates Munsang Kim for his leading projects that have E.52 Modeling and Control produced many of research achievements related to of Aerial Robots Sect. 58.3 enabling technologies. by Robert Mahony, Randal Beard, Vijay Kumar All three authors acknowledge comments from The authors would like to acknowledge the contribu- Prof. Cdric Pradalier of Georgia Tech Lorraine and tions of Peter Corke to the background material on Mark Noakes of the Oak Ridge National Laboratory in quadrotor vehicles (R. Mahony, V. Kumar, P. Corke: USA. Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor, Robotics Autom. Mag. 19(3), 20– F.59 Robotics in Mining 32 (2013)) and Eric Feron and Eric Johnson (E. Feron, by Joshua Marshall, Adrian Bonchis, Eduardo E. Johnson: Aerial robotics. In: Springer Handbook Nebot, Steven Scheding of Robotics, ed. by B. Siciliano, O. Khatib (Springer, The authors would like to thank Elliot Duff at CSIRO Berlin, Heidelberg 2008)) to the introductory material for his work in mustering the team for this chapter in this chapter. project. Thanks to Andrew Crose and Michael Lewis and Modular Mining Systems for help in obtaining F.57 Robotics in Construction information about the Komatsu AHS. Thanks also to by Kamel Saidi, Thomas Bock, Johan Larsson, Ola Pettersson, and Oscar Lundhede for Christos Georgoulas facilitating the use of some Atlas Copco AB images and Disclaimer: Although certain commercial construction video. equipment are included in this chapter, the inclusion of such information should in no way be construed as F.62 Intelligent Vehicles indicating that such products are endorsed by the Na- by Alberto Broggi, Alex Zelinsky, Ümit Özgüner, tional Institute of Standards and Technology (NIST) or Christian Laugier are recommended by NIST or that they are necessarily The authors acknowledge the contributions of Chuck the best equipment for the purposes described. Thorpe and Michel Parent, who made significant contri- 2162 Acknowledgements Acknowl. butions to the first edition of the handbook chapter, and Rocco Vertechy for their helpful assistance and sugges- part of their contributions remain in the revised second tions in writing this manuscript. edition. We also acknowledge the helpful contributions of Dizan Alejandro Vasquez. G.74 Learning from Humans by Aude Billard, Sylvain Calinon, G.70 Human–Robot Augmentation Rüdiger Dillmann by Massimo Bergamasco, Hugh Herr We warmly thank Daniel Grollman and Stefan Schaal The authors wish to thank Jared Markowitz, Elliott for participation in drafting earlier versions of this Rouse, Carlo Alberto Avizzano, Marco Fontana, and chapter. 2163 About the Authors Markus W. Achtelik Chapter B.26 ETH Zurich Markus Wilhelm Achtelik received his Diploma degree in Electrical Engineering Authors Autonomous Systems Laboratory from the TU München in 2009. He finished his PhD in 2014 at the Autonomous Zurich, Switzerland Systems Lab (ASL) at ETH Zurich, and currently works as Postdoc at ASL on control, [email protected] state estimation and planning, with the goal of enabling autonomous manoeuvres for MAVs, using an IMU and onboard camera(s) as main sensors. Alin Albu-Schäffer Chapter B.21 DLR Institute of Robotics and Alin Albu-Schäffer graduated in Electrical Engineering from the Technical University Mechatronics of Timisoara, in 1993 and got the PhD from the TU Munich in 2002. Since 2012 he Wessling, Germany is the Head of the Institute of Robotics and Mechatronics at the German Aerospace [email protected] Center, which he joined in 1995 as a PhD candidate. Moreover, he is a Professor at the TU Munich’s Computer Science Department. His research interests include robot design, modeling and control, flexible joints, as well as bio-inspired robot design. Kostas Alexis Chapter B.26 ETH Zurich Kostas Alexis obtained his PhD in the field of aerial robotics control and Institute of Robotics and Intelligent collaboration from the University of Patras, Greece in 2011. Since then Systems he holds a senior researcher position at ASL – ETH Zurich. His research Zurich, Switzerland interests lie in control and optimization focusing on aerial systems [email protected] navigation. He is the co-author of more than 40 technical publications. Jorge Angeles Chapter B.16 McGill University Jorge Angeles graduated as an Electromechanical Engineer and obtained Department of Mechanical Engineering the MEng degree in Mechanical Engineering, both at Universidad and Centre for Intelligent Machines Nacional Autónoma de México (UNAM), then received the PhD degree Montreal, Canada in Applied Mechanics from Stanford University. Research activities [email protected] include robot kinematics, dynamics, design, and control as well as design theory and methodology. Angeles is a Fellow of ASME, CSME, IEEE and RSC, The Academies of Arts, Humanities, and Sciences of Canada, and Honorary Member of IFToMM, the International Federation for the Promotion of Mechanism and Machine Science. Gianluca Antonelli Chapter E.51 For biographical details see “About the Multimedia Editors” Fumihito Arai Chapter B.27 Nagoya University Fumihito Arai received Master of Eng. degree from Tokyo University of Science in Department of Micro-Nano Systems 1988. He received Dr. of Eng. from Nagoya University in 1993. Since 1994, he was Engineering Assistant Professor at Nagoya University, from 2005 Professor at Tohoku University. Nagoya, Japan Since 2010 he has been Professor at Nagoya University mainly engaged in micro- and [email protected] nano-robotics. Michael A. Arbib Chapter G.77 University of Southern California Michael Arbib’s group at the University of Massachusetts Amherst (UMass) in- Computer Science, Neuroscience and ABLE troduced the notions of opposition space and affordances to the study of brain Project mechanisms for primate and control of robot hands. At the University of Southern Los Angeles, USA California, his group developed new models of primate visuomotor coordination, [email protected] including the first computational model of mirror neurons. He continues to develop the mirror system hypothesis for the evolution of the language-ready brain. 2164 About the Authors J. Andrew Bagnell Chapter A.15 Carnegie Mellon University Dr. J. Andrew Bagnell is an Associate Research Professor at Carnegie Robotics Institute Mellon University’s Robotics Institute and National Robotics Engineering Pittsburgh, USA Center (NREC) and the Machine Learning Department. His research fo- [email protected] cuses on machine learning and perception, adaptive control, optimization and planning under uncertainty. He received a BSc in Electrical Engi- neering from the University of Florida in 1998. He joined the Robotics Institute at CMU in 2000 receiving an MS and PhD in Robotics in 2002 Authors and 2004, respectively. Randal W. Beard Chapter E.52 Brigham Young University Randal Beard is a Professor in the Department of Electrical and Electrical and Computer Engineering Computer Engineering at Brigham Young University. He received Provo, USA his PhD from Rensselaer Polytechnic Institute. His research interests [email protected] include guidance and control of teams of unmanned air vehicles. He is a Fellow of the IEEE and an Associate Fellow of AIAA. Michael Beetz Chapter A.14 University Bremen Michael Beetz is a Professor for Computer Science at the Faculty for Informatics of Institute for Artificial Intelligence the University Bremen and Head of the Institute for Artificial Intelligence. He received Bremen, Germany his diploma degree in Computer Science from
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