Artificial Intelligence for Future Generation Robotics

Helps readers imagine the future of smart robotics using Artificial Intelligence (AI)

Rabindra Nath Shaw (Edited by), Ankush Ghosh (Edited by), Valentina Emilia Balas (Edited by), Monica Bianchini (Edited by)

9780323854986, Elsevier Science

Paperback / softback, published 7 July 2021

178 pages, 50 illustrations (25 in full color)
22.9 x 15.2 x 1.3 cm, 0.29 kg

Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation.

1.Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning 2.Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques 3.Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network 4.Single shot detection for detecting real-time flying objects for unmanned aerial vehicle 5.Depression detection for elderly people using AI robotic systems leveraging the Nelder-Mead Method 6.Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder-Mead method 7.Advance machine learning and artificial intelligence applications in service robot 8.Integrated deep learning for self-driving robotic cars 9.Lyft 3D object detection for autonomous vehicles 10.Recent trends in pedestrian detection for robotic vision using deep learning techniques

Subject Areas: Artificial intelligence [UYQ], Robotics [TJFM1]