IEEE Robotics & Automation Magazine 

About: IEEE Robotics & Automation Magazine is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Robot & Robotics. It has an ISSN identifier of 1070-9932. Over the lifetime, 1258 publications have been published receiving 75270 citations. The journal is also known as: Institute of Electrical and Electronics Engineers robotics & automation magazine & IEEE robotics and automation magazine.

Simultaneous localization and mapping: part I

Abstract: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association

The dynamic window approach to collision avoidance

Abstract: This approach, designed for mobile robots equipped with synchro-drives, is derived directly from the motion dynamics of the robot. In experiments, the dynamic window approach safely controlled the mobile robot RHINO at speeds of up to 95 cm/sec, in populated and dynamic environments.

Simultaneous localization and mapping (SLAM): part II

Abstract: This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation

The Uncanny Valley [From the Field]

Abstract: More than 40 years ago, Masahiro Mori, a robotics professor at the Tokyo Institute of Technology, wrote an essay [1] on how he envisioned people's reactions to robots that looked and acted almost like a human. In particular, he hypothesized that a person's response to a humanlike robot would abruptly shift from empathy to revulsion as it approached, but failed to attain, a lifelike appearance. This descent into eeriness is known as the uncanny valley. The essay appeared in an obscure Japanese journal called Energy in 1970, and in subsequent years, it received almost no attention. However, more recently, the concept of the uncanny valley has rapidly attracted interest in robotics and other scientific circles as well as in popular culture. Some researchers have explored its implications for human-robot interaction and computer-graphics animation, whereas others have investigated its biological and social roots. Now interest in the uncanny valley should only intensify, as technology evolves and researchers build robots that look human. Although copies of Mori's essay have circulated among researchers, a complete version hasn't been widely available. The following is the first publication of an English translation that has been authorized and reviewed by Mori. (See “Turning Point” in this issue for an interview with Mori.).

System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf]

Abstract: Roboticists are increasingly dealing with challenging complex problems in system identification for model-based control, and this book lays a foundation of knowledge for the reader to absorb, which can help address the said challenges. That being said, the reader should prepare for a challenge when reading this book. Though it is an excellent text, it is not for the casual reader. A background in Fourier series and Laplace transforms helps as well,though Ljung does a good job with the early chapters, providing an overview of the concepts that will be used throughout the book. This book covers parametric and nonparametric methods, parameter estimation methods in the prediction error framework, frequency domain data and interpretations, various ways to compute estimates, recursive estimation techniques, model validation,and case studies. Each chapter ends with an extensive bibliography for further reading and, often, has an appendix for proofs and derivations. The author presents a thorough and complete picture of system identification as a methodology, set of tools,and practical approach to generating models from data. The author's work has been influential for decades, and anyone interested in buildingmodels from data would be well served to put the effort into mastering thismaterial.