Artificial Intelligence (AI) techniques are data-centric approaches that can inspire decision support across areas like Robotics, physical intelligent agents, Cyber-physical Robotic systems and others. This inspiration occurs by coordinating data delivery and analyzing data trends, providing forecasts/insights, developing data consistency, quantifying uncertainty, anticipating the user’s data needs and then providing appropriate information to the user while suggesting courses of action. Computational AI techniques and the new evolution of computing and processing capabilities introduced by high-performance CPU/GPU components have widely impacted the robotics industry. In addition, the revolution of the Internet of Things (IoT), industrial IoT, and Cyber-Physical Systems (CPS) have contributed to developing highly intelligent autonomous robots and physical intelligent agents. These developments have improved the robotics industry and enhanced human-robot interaction and robot-to-robot interaction through new intelligent and communication technologies.
The growing popularity of AI has urged several businesses to invest in developing and researching different AI applications. There is no denying that implementing AI can have several challenges. Adopting a step-by-step strategic approach will simplify the process of AI implementation to a certain level. However, it is important to note that AI faces many challenges, such as determining the right data set, data security and storage, infrastructure, AI integration and deployment into existing systems, and the complex algorithms and training of AI models.
As both the industry and technology move toward smart technologies at lower costs, the main goal of this Research Topic is to bring together contributions from cutting-edge academic and industry researchers to present the most recent research findings in the rapidly developing field of analytics. It also highlights developments in the design of robotics, physical intelligent agents, and Cyber-physical Robotic systems that use AI techniques, such as machine learning, deep learning, federated learning, Bayesian networks, neural networks, fuzzy logic self-organizing maps, and others. The Research Topic also focuses on improving decision-making processes toward solving difficult applied problems that involve large amounts of data, that are usually real-time, and benefit from complex reasoning in robotics, physical intelligent agents, and Cyber-physical Robotic systems.
Topics of Interest Include:
• Intelligent design for real-time applications for Robotics and other physical intelligent agents.
• Intelligent decision support/making systems for Robotics and other physical intelligent agents.
• Intelligent Intrusion detection and mitigation systems for the Robotics, physical intelligent agents, and Cyber-physical Robotic systems.
• Intelligent techniques for the dependability of systems based on emerging computing paradigms and technologies.
• Intelligent lightweight solutions for Robotics, UAVs, physical intelligent agents, and IoT applications.
• The utilization of AI-driven blockchain for enhancing applications in robots/physical intelligent agents.
• Computational Intelligence Applications for Secure Robotics, physical intelligent agents, and Cyber-physical Robotic systems.
• Cognitive Artificial Intelligence Countermeasure For Enhancing The Hardware Security Of Robotics and Cyber-physical Robotic systems From Power/time Analysis Attack
• AI-driven Analysis of real-time vulnerabilities, attacks, and security for robotics, UAVs, physical intelligent agents, Cyber-physical Robotic systems, and IoT ecosystems.
Artificial Intelligence (AI) techniques are data-centric approaches that can inspire decision support across areas like Robotics, physical intelligent agents, Cyber-physical Robotic systems and others. This inspiration occurs by coordinating data delivery and analyzing data trends, providing forecasts/insights, developing data consistency, quantifying uncertainty, anticipating the user’s data needs and then providing appropriate information to the user while suggesting courses of action. Computational AI techniques and the new evolution of computing and processing capabilities introduced by high-performance CPU/GPU components have widely impacted the robotics industry. In addition, the revolution of the Internet of Things (IoT), industrial IoT, and Cyber-Physical Systems (CPS) have contributed to developing highly intelligent autonomous robots and physical intelligent agents. These developments have improved the robotics industry and enhanced human-robot interaction and robot-to-robot interaction through new intelligent and communication technologies.
The growing popularity of AI has urged several businesses to invest in developing and researching different AI applications. There is no denying that implementing AI can have several challenges. Adopting a step-by-step strategic approach will simplify the process of AI implementation to a certain level. However, it is important to note that AI faces many challenges, such as determining the right data set, data security and storage, infrastructure, AI integration and deployment into existing systems, and the complex algorithms and training of AI models.
As both the industry and technology move toward smart technologies at lower costs, the main goal of this Research Topic is to bring together contributions from cutting-edge academic and industry researchers to present the most recent research findings in the rapidly developing field of analytics. It also highlights developments in the design of robotics, physical intelligent agents, and Cyber-physical Robotic systems that use AI techniques, such as machine learning, deep learning, federated learning, Bayesian networks, neural networks, fuzzy logic self-organizing maps, and others. The Research Topic also focuses on improving decision-making processes toward solving difficult applied problems that involve large amounts of data, that are usually real-time, and benefit from complex reasoning in robotics, physical intelligent agents, and Cyber-physical Robotic systems.
Topics of Interest Include:
• Intelligent design for real-time applications for Robotics and other physical intelligent agents.
• Intelligent decision support/making systems for Robotics and other physical intelligent agents.
• Intelligent Intrusion detection and mitigation systems for the Robotics, physical intelligent agents, and Cyber-physical Robotic systems.
• Intelligent techniques for the dependability of systems based on emerging computing paradigms and technologies.
• Intelligent lightweight solutions for Robotics, UAVs, physical intelligent agents, and IoT applications.
• The utilization of AI-driven blockchain for enhancing applications in robots/physical intelligent agents.
• Computational Intelligence Applications for Secure Robotics, physical intelligent agents, and Cyber-physical Robotic systems.
• Cognitive Artificial Intelligence Countermeasure For Enhancing The Hardware Security Of Robotics and Cyber-physical Robotic systems From Power/time Analysis Attack
• AI-driven Analysis of real-time vulnerabilities, attacks, and security for robotics, UAVs, physical intelligent agents, Cyber-physical Robotic systems, and IoT ecosystems.
