About this Research Topic
With the continuous progress of Artificial Intelligence (AI), Space-Air-Ground Robotics (SAGR) achieved revolutionary development in various fields (interplanetary communication, space exploration, remote sensing, transportation, agriculture, smart city, disaster rescue, etc.), with outstanding performances in accuracy, robustness and efficiency. However, AI-enabled robotics rely on the advanced progress of Machine Learning (ML), Deep Learning (DL), Space-Air-Ground Communication (SAGC) and cloud computing technologies which seriously extend the global services and applications conveniently and intelligently.
As missions grow longer and more complex, SAGRs with various functions finish the specific missions (detection, sensing, computing, perception, etc.) automatically which extends the scale of operations in space, air and ground via SAGC. Different types of SAGR with various mobilities (space, air and ground) collaborate with each other to provide services that enable operators to obtain a global optimization for missions. Powered by AI, SAGR can achieve missions with high requirements which improves the functionalities of SAGR maximally. AI enabling technologies accelerate the intelligence development of SAGR which also promotes the ubiquitous implementations of field robotics in different fields.
Against this background, AI for SAGR is drawing increasing attention from both researchers and industries. The optimal implementations and development of AI for SAGR can stimulate the progress of other fields (aeronautics, medical care, agriculture, transportation, industrial applications, educations, etc.) dramatically. The advanced applications of SAGR brings challenges to existing ML/DL, SAGR and SAGC.
This Research Topic is inviting publications covering topics relating to AI for SAGR applications and enabling technologies (particularly those emphasizing SAGR on sensing, computing, perception, space-air-ground communication, explainable and assurable AI and swarm intelligence).
The scope of this Research Topic includes, but is not limited to, the following topics:
• AI-enabled systems for space-air-ground robotics
• AI for autonomous space-air-ground robotics
• AI-enabled space-air-ground robotics for sensing applications
• AI-enabled space-air-ground robotics for computing
• AI-enabled space-air-ground robotics for smart city
• AI-enabled space-air-ground robotics for disaster rescue
• Explainable and assurable AI for space-air-ground robotics
• Swarm intelligence paradigms for space-air-ground robotics
• AI-enabled space-air-ground robotics for sensing applications including advance multi-modal Radar technologies
• AI-enabled space-air-ground robotics for computing and data storage, processing and information retrieval.
As missions grow longer and more complex, SAGRs with various functions finish the specific missions (detection, sensing, computing, perception, etc.) automatically which extends the scale of operations in space, air and ground via SAGC. Different types of SAGR with various mobilities (space, air and ground) collaborate with each other to provide services that enable operators to obtain a global optimization for missions. Powered by AI, SAGR can achieve missions with high requirements which improves the functionalities of SAGR maximally. AI enabling technologies accelerate the intelligence development of SAGR which also promotes the ubiquitous implementations of field robotics in different fields.
Against this background, AI for SAGR is drawing increasing attention from both researchers and industries. The optimal implementations and development of AI for SAGR can stimulate the progress of other fields (aeronautics, medical care, agriculture, transportation, industrial applications, educations, etc.) dramatically. The advanced applications of SAGR brings challenges to existing ML/DL, SAGR and SAGC.
This Research Topic is inviting publications covering topics relating to AI for SAGR applications and enabling technologies (particularly those emphasizing SAGR on sensing, computing, perception, space-air-ground communication, explainable and assurable AI and swarm intelligence).
The scope of this Research Topic includes, but is not limited to, the following topics:
• AI-enabled systems for space-air-ground robotics
• AI for autonomous space-air-ground robotics
• AI-enabled space-air-ground robotics for sensing applications
• AI-enabled space-air-ground robotics for computing
• AI-enabled space-air-ground robotics for smart city
• AI-enabled space-air-ground robotics for disaster rescue
• Explainable and assurable AI for space-air-ground robotics
• Swarm intelligence paradigms for space-air-ground robotics
• AI-enabled space-air-ground robotics for sensing applications including advance multi-modal Radar technologies
• AI-enabled space-air-ground robotics for computing and data storage, processing and information retrieval.
Keywords: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Space-Air-Ground Robotics (SAGR), Wireless Networks
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.
