Artificial Intelligence-Based Guidance, Navigation, and Control Technologies for Multiple Mobile Robotic Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 565
Special Issue Editors
Interests: control theory; application of multiple robotic systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The mobile robotic system is a systematic concept including ground vehicles, aerial vehicles, shipping equipment, and other mobile vehicles, as well as their interactions with people, facilities, and the environment. In recent years, the breakthrough in artificial intelligence (AI) technologies of multiple mobile robotic systems (MMRSs) has found great use in various applications, both in military and civil aspects, including, but not limited to, swarm combat, surveillance, inspection, precision agriculture, criminal investigations, search and rescue, weather measurement and forecasting, and disaster relief. In particular, AI-based guidance, navigation, and control (GNC) technologies are very important for MMRSs to adapt to the complicated hostile environment and enhance its survival capability, including methods of reinforcement learning, swarm optimization, deep learning, etc. However, most of the existing methods are just designed for simple dynamic systems with simple mission orientation. They cannot be directly migrated to the real complex scenes, and the related AI-based GNC methods need to be improved.
This Special Issue will gather the latest research results in the area of tackling the military and civil missions of MMRSs, with an emphasis on AI-based GNC methods. We invite researchers to contribute original research articles and comprehensive review articles. Topics include but are not limited to the following:
- Intelligent swarm and flocking control;
- Deep learning target detection and tracking;
- GPS-denied indoor and outdoor navigation;
- Deep reinforcement learning path planning;
- Deep reinforcement learning task planning.
Dr. Yang Xu
Dr. Delin Luo
Guest Editors
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Keywords
- intelligent control
- intelligent optimization
- deep learning
- reinforcement learning
- multi-agent systems
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