Advanced Control and Intelligent Decision-making Technologies for Robotic Systems

The fusion of advanced control and decision techniques with robotics has ushered in a new era of perceptual intelligence and autonomous navigation capabilities for robotic systems. By integrating advanced control and intelligent decision technologies, robots can navigate and comprehend complex environments. Robust control and optimal strategies enhance robots' ability to perform tasks efficiently and accurately adaptive to changing conditions. Employing intelligent algorithms such as learning-based strategies makes it possible for robots to understand and respond to complex commands, and then achieve enhanced decision-making and more adaptive problem-solving skills. The latest machine learning theories and technologies further promote the adaptability of robots in complex dynamic problems and the autonomy of task execution, exhibiting the advantages and development potential that traditional methods cannot achieve. The potential applications span across various fields, including manufacturing, industries, and indoor or outdoor service where robots can significantly improve efficiency and performance.

This Research Topic aims to investigate the transformative impact of merging advanced control and intelligent decision-making technologies in robotic systems. The overarching goal is to explore advanced robust control and system optimization techniques that can enhance the efficiency, adaptability, and performance of robots in various applications. By fostering collaboration among experts in control theory, robotics, and artificial intelligence, this research initiative aspires to advance the current understanding of how these technologies can collectively propel the field of robotics forward, empowering robots to operate with heightened precision, flexibility, and intelligence across diverse scenarios and challenges. Meanwhile, interdisciplinary collaboration is also encouraged to create creative new theories and technologies, providing valuable exploration experience for the development of robots towards a more intelligent and versatile direction.

We invite researchers and practitioners to contribute original research, review articles, case studies, and technical notes that address the following themes:
1. Robot manipulation and robot in-wheel-driven techniques
2. Learning-based navigation, visual navigation, and map-less navigation
3. Intelligent decision, autonomous environments exploring
4. Robust control, adaptive control, and system optimization
5. Cooperative and consensus control of multi-robot systems

Authors are encouraged to submit manuscripts that provide new insights, theoretical advancements, or practical implementations of advanced control and decision-making algorithms or architectures that can handle the complexity and nonlinearity inherent in robotic systems, enabling precise and responsive control.

Keywords: robot manipulation and robot in-wheel-driven techniques, learning-based navigation/recognition/sensing, intelligent decision-making, environments exploring and understanding, robust control, adaptive control and system optimization, cooperative and consen

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.