Bio-inspired locomotion control of Limbless robots

This book presents a bio-inspired hierarchical control scheme step by step toward developing limbless robots capable of 3D locomotion, fast reflex response, as well as sophisticated reaction to environmental stimuli. This interdisciplinary book introduces how to combine biological concept with locomotion control of limbless robots. The special features of the book include limbless locomotion classification and control, design of biological locomotor and the integration of sensory information into the locomotor using artificial intelligence methods, and on-site demonstrations of limbless locomotion in different scenarios. The book is suitable for readers with engineering background, especially for researchers focused on bio-inspired robots.

Guoyuan Li is a full professor at the Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology. He received his Ph.D. degree in computer science from the Institute of Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany, in 2013. Since 2014, he has been with the Intelligent Systems Laboratory, Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Ålesund, Norway. He became an associate professor of ship intelligence, and further promoted to full professor at Department of Ocean Operations and Civil Engineering, NTNU, Ålesund, Norway in 2018 and 2021, respectively. Dr. Li has published more than 90 papers in the areas of his research interests which include locomotion control of bioinspired robots, digitalization, simulation and modelling, eye tracking analysis, and artificial intelligence and optimization algorithms in marine operation. Dr. Li has received one best paper award and two finalist award at IEEE international conferences.

Houxiang Zhang is a full professor and director of Intelligent Systems Laboratory, at the Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology. He received his Ph.D. degree on Mechanical and Electronic Engineering in 2003. From 2004, he worked as Postdoctoral fellow, senior researcher at the Institute of Technical Aspects of Multimodal Systems (TAMS), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. In Feb. 2011, he finished the Habilitation on Informatics at University of Hamburg. Dr. Zhang joined the NTNU, Norway in April 2011 where he is a full Professor on Mechatronics. From 2011 to 2016, Dr. Zhang also hold a Norwegian national GIFT Professorship on product and system design funded by Norwegian MaritimeCentre of Expertise. In 2019, Dr. Zhang has been elected to the member of Norwegian Academy of Technological Sciences. Dr. Zhang has engaged into two main research areas including control, optimization and AI application especially on autonomous vehicle; and marine automation, digitalization and ship intelligence. He has applied for and coordinated more than 30 projects supported by Norwegian Research Council (NFR), German Research Council (DFG), EU, and industry. In these areas, he has published over 300 journal and conference papers as author or co-author. Dr. Zhang has received four best paper awards, and six finalist awards for best conference paper at International conference on Robotics and Automation.​

Jianwei Zhang is a full professor and director of TAMS, Department of Informatics, University of Hamburg, Germany. He received both his Bachelor of Engineering (1986, with distinction) and Master of Engineering (1989) at the Department of Computer Science of TsinghuaUniversity, Beijing, China, his PhD (1994) at the Institute of Real-Time Computer Systems and Robotics, Department of Computer Science, University of Karlsruhe, Germany. Jianwei Zhang is life-long Academician of Academy of Sciences in Hamburg Germany. His research interests include multimodal information processing; cognitive sensor fusion; fast learning algorithms; neuro-fuzzy models for sensory-motor control tasks; reinforcement learning of assembly sequences; natural human-robot interaction; self-valuing learning of robot grasping and in-hand manipulation; multimodal learning architecture; experience-based robot learning; coded structured light for 3D modelling; best view algorithm for active robot vision; bio-inspired multimodal control, modular reconfigurable robots; surgical assistant robots; mobile manipulation service robots, etc. In these areas he has published about 500 journal and conference papers, technical reports and four books. He holds 40+ patents on intelligent components and systems.