Emanuele Menegatti, Nathan Michael, Karsten Berns, Hiroaki's Intelligent Autonomous Systems 13: Proceedings of the 13th PDF

By Emanuele Menegatti, Nathan Michael, Karsten Berns, Hiroaki Yamaguchi

ISBN-10: 3319083376

ISBN-13: 9783319083377

ISBN-10: 3319083384

ISBN-13: 9783319083384

This booklet describes the most recent learn accomplishments, options, and visions within the box of robotics as provided on the thirteenth foreign convention on clever self sustaining platforms (IAS), held in Padua in July 2014, via top researchers, engineers, and practitioners from the world over. The contents amply ensure that robots, machines, and structures are swiftly attaining intelligence and autonomy, getting to know progressively more features reminiscent of mobility and manipulation, sensing and conception, reasoning, and determination making. a variety of study effects and purposes are lined, and specific awareness is paid to the rising function of self sustaining robots and clever platforms in commercial creation, which displays their adulthood and robustness. The contributions were chosen via a rigorous peer-review procedure and comprise many interesting and visionary rules that may extra impress the examine neighborhood, spurring novel study instructions. The sequence of biennial IAS meetings started out in 1986 and represents a best occasion in robotics.

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Additional info for Intelligent Autonomous Systems 13: Proceedings of the 13th International Conference IAS-13

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Davide A. Cucci, Martino Migliavacca, Andrea Bonarini and Matteo Matteucci Whole-Body Robot Motion Learning by Kinesthetic Teaching . . . Hsien-I Lin, Yung-Yao Chen and Yu-Che Huang 1437 1451 1467 xxxiv Experiment of Stereo Sensors for Chemical Plume Tracing by Optogenetic Silkworm Moth . . . . . . . . . . . . . . Kotaro Kishi, Daisuke Kurabayashi, Ryo Minegishi, Takeshi Sakurai, Ryohei Kanzaki, Masashi Tabuchi and Hideki Sezutsu Distributed Algorithm for Robotic Network Self-deployment in Indoor Environments Using Wireless Signal Strength .

1) is approximated as N B(X ) = w (i) δ(x (i) ∈ X ). (7) i=1 When the robot obtains a set of measurement values ( , ϕ) from the landmark observation, the weight of each particle is changed to w (i) =α −1 w (i) N ( − (i) , σ 2 )N (ϕ − ϕ (i) , σϕ2 ) (8) where N α= w (i) , and (9) i=1 1 x2 N (x, σ ) = √ exp − 2 . 2σ 2π σ 2 (10) ( (i) , ϕ (i) ) are the direction and the orientation of the landmark from x (i) . σ and σϕ are the standard deviations of the measurement of and ϕ respectively. Particles frequently converge on some points where the robot does not exist.

Fabio DallaLibera, Christian I. Penaloza, Yuichiro Yoshikawa and Hiroshi Ishiguro Dynamic Simulation of Robotic Devices Using the Biomechanical Simulator OpenSim . . . . . . . . . . . . . . . . . . . Michele Vivian, Luca Tagliapietra, Massimo Sartori and Monica Reggiani Part XI 1601 1613 1627 1639 Industrial Applications RGB-D Human Detection and Tracking for Industrial Environments . . . . . . . . . . . . . . . . . . . . . Matteo Munaro, Christopher Lewis, David Chambers, Paul Hvass and Emanuele Menegatti Architecture Challenges for Intelligent Autonomous Machines.

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Intelligent Autonomous Systems 13: Proceedings of the 13th International Conference IAS-13 by Emanuele Menegatti, Nathan Michael, Karsten Berns, Hiroaki Yamaguchi


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