Author
Jon Rigelsford
Bio: Jon Rigelsford is an academic researcher. The author has contributed to research in topics: Robotics & Welding. The author has an hindex of 13, co-authored 24 publications receiving 1311 citations.
Topics: Robotics, Welding, Rapid prototyping, Mechatronics, Adaptable robotics
Papers
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TL;DR: In this article, the authors tried to read modelling and control of robot manipulators as one of the reading material to finish quickly, and they found that reading book can be a great choice when having no friends and activities.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.
517 citations
350 citations
119 citations
84 citations
58 citations
Cited by
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01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.
6,340 citations
TL;DR: An improved typology of C&P problems is presented, which is partially based on Dyckhoff’s original ideas, but introduces new categorisation criteria, which define problem categories different from those of Dykhoff.
1,359 citations
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TL;DR: The recycling of post-consumer PET (POSTC-PET) as a technology is a cross-disciplinary practice with many fields of science involved including polymer chemistry and physics, process engineering and manufacturing engineering.
731 citations
TL;DR: A generalised and extended decision matrix is constructed and rule and utility based techniques are developed for transforming various types of information within the matrix for aggregating attributes via ER that are equivalent with regard to underlying utility and rational in terms of preserving the features of original assessments.
710 citations
01 Oct 2006
TL;DR: An overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field is given and how the most recently developed methods can significantly improve learning performance is shown.
Abstract: The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm
598 citations