: Honghai Liu, Dongbing Gu, Yonghuai Liu
: Honghai Liu, Dongbing Gu, Robert J. Howlett, Yonghuai Liu
: Robot Intelligence An Advanced Knowledge Processing Approach
: Springer-Verlag
: 9781849963299
: Advanced Information and Knowledge Processing
: 1
: CHF 133.60
:
: Anwendungs-Software
: English
: 294
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF
Robot intelligence has become a major focus of intelligent robotics. Recent innovation in computational intelligence including fuzzy learning, neural networks, evolutionary computation and classical Artificial Intelligence provides sufficient theoretical and experimental foundations for enabling robots to undertake a variety of tasks with reasonable performance. This book reflects the recent advances in the field from an advanced knowledge processing perspective; there have been attempts to solve knowledge based information explosion constraints by integrating computational intelligence in the robotics context.

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 identification; virtual sensors; Automotive electronics: engine management systems; monitoring and control of small engines. He is the Executive Chair of the UKES Internationalorganization, which facilitates knowledge transfer and research in areas including Intelligent Systems, Sustainability, and Knowledge Transfer. Through the UKES Smart Systems Centre he provides consultancy services on, for example, Knowledge Transfer Partnerships the EU Interreg Anglo-French funding programme, and technical subjects within his expertise. By setting up and managing over 20 collaborative projects with SMEs and other companies, managing the University of Brighton Knowledge Transfer Partnerships (KTP) Centre for a number of years, and Chairmanship of the KTP National Forum, he has become nationally recognised in knowledge and technology transfer, the commercialisation of research, and the third-mission agenda.

Dr Yonghuai Liu has completed BSc and MSc studies and also holds two PhDs. Younghuai gained solid knowledge in the fields of Geography, Cartography, Mathematics, and Economics whilst studying for the BSc degree. Whilst studying for the MSc degree he gained knowledge in the fields of Pattern Recognition, Image Processing, and Mathematics. PhD study in China which gave him solid knowledge in the fields of Artificial Intelligence, Uncertain Reasoning, and also Mathematics. During this period of time, Yonghuai researched on Uncertain Reasoning, Expert Systems, Artificial Intelligence, Pattern Recognition, Image Processing, and Multimedia and taught both undergraduate and postgraduate courses on Artificial Intelligence, Discrete Mathematics, Combinatorial Mathematics, and Multimedia. Younghuai received the ORS award. As a result, he studied for his second PhD degree at The University of Hull under the supervision of Dr Marcos A Rodrigues. He is currently a lecturer at the Department of Computer Science, The University of Wales, Aberystwyth.

Preface4
Contents8
Contributors10
Programming-by-Demonstration of Robot Motions13
Introduction13
Learning from Human Demonstration15
Interpretation of Demonstrations in Hand-State Space15
Skill Encoding Using Fuzzy Modeling16
Generation and Execution of Robotic Trajectories Based on Human Demonstration18
Mapping Between Human and Robot Hand States19
Definition of Hand-States for Specific Robot Hands20
Next-State-Planners for Trajectory Generation22
Demonstrations of Pick-and-Place Tasks24
Variance from Multiple Demonstrations24
Experimental Platform24
Experimental Evaluation26
Experiment 1: Learning from Demonstration26
Importance of the Demonstration27
Experiment 2: Generalization in Workspace29
Experiment 3: a Complete Pick-and-Place Task32
Conclusions and Future Work32
References34
Grasp Recognition by Fuzzy Modeling and Hidden Markov Models36
Introduction36
An Experimental Platform for PBD37
Simulation of Grasp Primitives39
Geometrical Modeling39
Modeling of Inverse Kinematics40
Modeling of Grasp Primitives42
Modeling by Time-Clustering42
Training of Time Cluster Models Using New Data43
Recognition of Grasps-Three Methods44
Recognition of Grasps Using the Distance Between Fuzzy Clusters44
Recognition Based on Qualitative Fuzzy Recognition Rules45
Distance Norms45
Extrema in the Distance Norms and Segmentation46
Set of Fuzzy Rules48
Similarity Degrees49
Recognition Based on Time-Cluster Models and HMM50
Experiments and Simulations53
Time Clustering and Modeling53
Grasp Segmentation and Recognition55
Conclusions57
References58
Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks59
Introduction59
Preliminaries61
Multi-Manipulator System Description61
Radial Basis Function Neural Network63
Controller Design65
Performance Analysis67
Simulation Example71
Conclusion75
References78
A New Framework for View-Invariant Human Action Recognition80
Introduction80
Overview of the Proposed Approach85
Exemplar Selection and Representation87
Key Pose Extraction87
2D Silhouette Image Generation88
Contour Shape Feature89
Action Modelling and Recognition91
Exemplar-based Hidden Markov Model91