Unmanned robot systems with high delivery and flexible maneuverability have recently attracted increasing research interest, especially in engineering applications. However, the interactive technology to execute autonomous navigation, exploration, tracking and other complex tasks in uncertain environments is still a key issue to be solved urgently. Fortunately, neuro-derived control is readily available with the rapid development of sensors, which brings an opportunity to reconstruct a reliable human-like framework to approximate human dynamics. Furthermore, the neural network is a powerful tool to reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Therefore, advanced neuro-derived control methods for interactive technology on mobile robot systems are inspiring and promising topics. This special issue aims to bring world-class researchers to present state-of-the-art research achievements and advances that contribute to neuro-derived control for interactive technology on unmanned robot systems in terms of control theory, environment perception, human-robot interaction, and sensor fusion. Review articles are also encouraged.
The potential topics of this organized session include, but are not limited to:
- Brain-derived signals processing for unmanned robot systems
- Brain-machine interfaces control for unmanned robot systems
- EGG-based teleoperation control for unmanned robot systems
- Advanced control theory for unmanned robot systems
- Integrating perception, sensor fusion and control for unmanned robot systems
- Deep learning techniques for robotic systems in perception and control
- Fault detection and diagnosis for unmanned robot systems
- Human-robot interaction for unmanned robot systems
Unmanned robot systems with high delivery and flexible maneuverability have recently attracted increasing research interest, especially in engineering applications. However, the interactive technology to execute autonomous navigation, exploration, tracking and other complex tasks in uncertain environments is still a key issue to be solved urgently. Fortunately, neuro-derived control is readily available with the rapid development of sensors, which brings an opportunity to reconstruct a reliable human-like framework to approximate human dynamics. Furthermore, the neural network is a powerful tool to reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Therefore, advanced neuro-derived control methods for interactive technology on mobile robot systems are inspiring and promising topics. This special issue aims to bring world-class researchers to present state-of-the-art research achievements and advances that contribute to neuro-derived control for interactive technology on unmanned robot systems in terms of control theory, environment perception, human-robot interaction, and sensor fusion. Review articles are also encouraged.
The potential topics of this organized session include, but are not limited to:
- Brain-derived signals processing for unmanned robot systems
- Brain-machine interfaces control for unmanned robot systems
- EGG-based teleoperation control for unmanned robot systems
- Advanced control theory for unmanned robot systems
- Integrating perception, sensor fusion and control for unmanned robot systems
- Deep learning techniques for robotic systems in perception and control
- Fault detection and diagnosis for unmanned robot systems
- Human-robot interaction for unmanned robot systems
