Robot Intelligence / Advanced Information and Knowledge Processing (PDF)

Dr. Honghai Liu is a Reader and Head of Intelligent Systems & Robotics Research Group (ISR), School of Creative Technologies, at the University of Portsmouth. He previously held research appointments at the Departments of Computing Science and Engineering in the Universities of London and Aberdeen, and project leader appointments in large-scale industrial control and system integration industry. Honghai has published over 150 refereed journal and conference papers including three Best Paper Awards. He is interested in approximate computation, machine intelligence, pattern recognition and their practical applications with an emphasis on approaches which could make contribution to the intelligent connection of perception to action in systems context. For this emphasis, he has been developing a framework based on approximate computing and it has been implemented into human motion analysis, multifingered robot manipulation, data novelty detection and intelligent control for electric vehicle suspensions with substantial results. He is a Senior Member of IEEE and a Member of IET.

Dongbing Gu's current research interests include multi-agent systems, wireless sensor networks, distributed control  algorithms, distributed information fusion, cooperative control, reinforcement learning, fuzzy logic and neural network based motion control, model predictive control, wavelet multi-scale image edge detection, and Bayesian multi-scale image segmentation. His work combines fundamental concepts and tools from computer science, networks, systems and control theory.

Robert Howlett has considerable expertise in the use of Intelligent Systems in the solution of industrial problems.  He has been successful in applying neural networks, expert & fuzzy methods, web intelligence and related technology to: Sustainability: renewable energy, measurement, control, simulation and modeling of energy systems; Condition monitoring: diagnostic tools and systems; fault location and

From the reviews:

“This book’s 13 chapters are a unique collection of research in which machine learning extends into robotic intelligence in order to develop robots that are more complex and adaptable to their dynamic environments. … There is something here for everyone great ideas, sufficient theoretical backups, and excellent case studies with real robots in action. Summing Up: Highly recommended. Upper-division undergraduates and above.” (G. Trajkovski, Choice, Vol. 48 (7), March, 2011)

“This book was a welcome surprise: it is short, but includes many topics relevant to the robotics research community. … Another interesting aspect of the book is that almost every chapter includes physical experiments. … Researchers and PhD students interested in the latest approaches and techniques in robotics will be most interested in this book. It is especially appropriate for researchers interested in the fields of manipulator robots and social robots (robots that interact with human beings).” (Ramon Gonzalez Sanchez, ACM Computing Reviews, January, 2012)