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Description
This book reports some latest human-robot collaboration technologies and manufacturing applications. Human-robot collaboration (HRC) is a promising new technology, in which humans and robots share their skills to establish joint capacities to perform complicated industrial jobs. The technology can meet the ever-increasing requirements for high-mixed/low-volume product manufacturing. In this book, foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. The book details innovative HRC design in terms of HRC safety assurance, robotic learning from demonstration, HRC activity planning and replanning. Manufacturing case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers.
The book offers a valuable resource for researchers in the intelligent manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in intelligent manufacturing and Industry 4.0.
Introduction.- Collaborative Robot Technologies for Industrial Tasks.- Learning from Demonstration for Autonomous Generation of Robot Trajectories.- Safe Human-Robot Collaboration for Industrial Settings.- End-of-Life Electric Vehicle Battery Disassembly Enabled by Intelligent and Human-Robot Collaboration Technologies.- An Improved Task-Parameterised Learning-from-Demonstration Approach for Collaborative Robots in Dynamic Manufacturing.- Optimised Learning from Demonstration for Collaborative Ro-bots.- Improved Deep Lagrangian Network-Enabled Momentum Ob-server for Collision Detection During Human-Robot Collaboration.- Dynamic Disassembly Planning of End-of-Life Products for Hu-man-Robot Collaboration Enabled by Multi-Agent Deep Rein-forcement Learning.- Human-Robot Collaborative Disassembly Enabled by Brain-waves and Improved Generative Adversarial Network.- Robotic Removal of Screws for End-of-Life Product Remanufac-turing Enabled by Deep Reinforcement Learning.- Adaptive Obstacle Avoidance in Path Planning of Collaborative Robots for Dynamic Manufacturing.
Weidong Li is currently the Chang Jiang Chair Professor and Dean of the School of Mechanical Engineering at the University of Shanghai for Science and Technology. From 2013, he served as a Full Professor in the School of Mechanical and Automotive Engineering at Coventry University (UK). Professor Li is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the Institution of Mechanical Engineers (IMechE), and a Senior Member of the IEEE. He has published approximately 300 peer-reviewed research papers in international journals and conferences, and authored or edited five books with renowned publishers, including Springer and World Scientific.
Duc Truong Pham holds the Chance Chair of Engineering at the University of Birmingham (UK). His research spans intelligent systems, robotics and autonomous systems, and advanced manufacturing technology. Professor Pham has published over 700 technical papers and books and supervised more than 100 PhD students to successful completion. He has secured in excess of £40 million in external research grants and industrial contracts. He is a Fellow of the Royal Academy of Engineering, the Learned Society of Wales, the Society of Manufacturing Engineers, the Institution of Engineering and Technology (IET), and the Institution of Mechanical Engineers (IMechE).
