About this Research Topic
In recent years, the applications of artificial intelligence (AI) to robotics have experimented with exponential growth. AI plays a crucial role in path planning of robots, allowing fast responses to changes in complex environments, which increases the autonomy of robots drastically. It also plays a leading role in modelling and intelligent control of robots by allowing a more complex feedback analysis, self-tuning applications, and on the fly adaptation to environmental changes.
Changing industrial environments like flexible manufacturing facilities and automated warehouses where robots are intended to work side by side with humans are benefiting directly from advancements in complex path planning and autonomous decision making based on AI-powered algorithms. On the consumer side, applications like cleaning robots and delivery robots are also becoming part of our daily lives. The implementation of AI-powered path planning drastically improves the efficiency and practicality of these robots, as the environments in which these robots must operate is highly dynamic and needs constant adaptation.
More recently, these advancements in AI had an extraordinary impact on mobile robotic platforms, where the autonomous car is leading the way. By making use of advanced machine learning techniques like neural networks, fuzzy systems, and deep learning, the platforms can make decisions and improve its programming autonomously. The research effort put into the autonomous car will lead the development in other autonomous applications. Other industrial applications like automated surveillance of installations with mobile robots or drones equipped with cameras will directly benefit from the technologies developed for the autonomous car.
To address the potential and growth of applications and opportunities in the use of AI for robot modelling, path planning, and intelligent control, we invite contributions focused on the applications of AI in robotics systems. We welcome all types of articles. Potential topics include, but are not limited to:
- AI-based path planning in dynamic environments with learning and adaptation
- AI applied in multi-agent robotic systems like mobile robot networks
- Robot control implementing intelligent control techniques
- Self-organization and Self-optimization via machine learning based AI
- System fault mitigation via adaptive and learning control techniques
- Practical applications and methods of AI in the industry
- Autonomous vehicle research and development based on AI
Changing industrial environments like flexible manufacturing facilities and automated warehouses where robots are intended to work side by side with humans are benefiting directly from advancements in complex path planning and autonomous decision making based on AI-powered algorithms. On the consumer side, applications like cleaning robots and delivery robots are also becoming part of our daily lives. The implementation of AI-powered path planning drastically improves the efficiency and practicality of these robots, as the environments in which these robots must operate is highly dynamic and needs constant adaptation.
More recently, these advancements in AI had an extraordinary impact on mobile robotic platforms, where the autonomous car is leading the way. By making use of advanced machine learning techniques like neural networks, fuzzy systems, and deep learning, the platforms can make decisions and improve its programming autonomously. The research effort put into the autonomous car will lead the development in other autonomous applications. Other industrial applications like automated surveillance of installations with mobile robots or drones equipped with cameras will directly benefit from the technologies developed for the autonomous car.
To address the potential and growth of applications and opportunities in the use of AI for robot modelling, path planning, and intelligent control, we invite contributions focused on the applications of AI in robotics systems. We welcome all types of articles. Potential topics include, but are not limited to:
- AI-based path planning in dynamic environments with learning and adaptation
- AI applied in multi-agent robotic systems like mobile robot networks
- Robot control implementing intelligent control techniques
- Self-organization and Self-optimization via machine learning based AI
- System fault mitigation via adaptive and learning control techniques
- Practical applications and methods of AI in the industry
- Autonomous vehicle research and development based on AI
Keywords: Artificial Intelligence, Robot Modelling, Path Planning, Control, Adaptive and Learning Control, Multi-Agent Robotic System, Autonomous Vehicle System
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
