Advancements in Neural Learning Control for Enhanced Multi-Robot Coordination

In the realm of modern industry, multi-robot systems such as autonomous vehicles, unmanned ships, and manipulators play a pivotal role due to their efficiency in handling complex, large-scale operations unsuitable for individual robots. The incorporation of artificial intelligence has led to significant advancements, particularly in the domains of learning and control. The application of neural networks in these systems facilitates the processing of vast datasets and the recognition of complex patterns, which are crucial for enhancing the operational capabilities of these robots. This symbiosis of neural learning and robotic control is creating smarter, more adaptable robotic systems.

This Research Topic aims to provide an academic forum for the dissemination of the latest advances in neural learning-based control for multi-robot systems. The focus is on promoting innovative research in dynamic learning-based controls, cooperative reinforcement learning, and the integration of machine learning techniques within multi-robot control frameworks. By showcasing cutting-edge research and experimental results, this topic seeks to foster rich discussions and further exploration of this promising field.

We invite original research articles that further advance knowledge and application in this area, contributions are sought that explore innovative neural learning-based control strategies. Relevant submissions will span a range of themes:

Dynamic learning from neural control for uncertain multi-robot systems
Cooperative (deep) reinforcement learning-based control for collaborative robot systems
Neural-network-based iterative learning control for multi-robot systems
Machine learning in multi-robot coordination control systems
Analysis of robustness and convergence within dynamic learning, reinforcement learning, iterative learning, and machine learning algorithms in robotic applications
These themes highlight the ongoing need to refine and implement neural learning mechanisms within multi-robot systems to meet the demands of contemporary industrial applications.

Keywords: Neural network control, Learning control, Dynamic learning, Reinforcement learning, Machine learning, Adaptive control, Robotics, Autonomous Vehicles, Unmanned ship, Unmanned aerial vehiclem Manipulators, Multi robotics

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.