Applied Mathematics in Robotic Mapping, Learning and Collaboration

Dear Colleagues,

Considering the recent development and transformation of robotics and machine learning, it is evident that we are at a pivotal moment in the advancement of intelligent systems. As robots become increasingly integrated into diverse sectors, including everyday life, there is a growing need to enhance their ability to understand their environment, navigate, learn and collaborate when they receive a task. Therefore, there is a significant opportunity to explore and consolidate advancements in this area.

The focus of this Special Issue, entitled "Applied Mathematics in Robotic Mapping, Learning and Collaboration", is to provide a comprehensive overview of the latest research and innovations in these interrelated domains. This Special Issue will concentrate on presenting cutting-edge advancements, methodologies, and novel approaches that contribute to the growth of robotic systems' mapping, learning, and collaborative abilities. The scope of this Special Issue encompasses a wide range of topics, including, but not limited to, mapping techniques, environment perception, deep learning algorithms for robot learning and adaptation, multi-robot coordination and collaboration, and human–robot interaction strategies. By including diverse perspectives and research areas, this Special Issue aims to offer a holistic understanding of the interconnected nature of mapping, learning, and collaboration in robotics.

This Special Issue will serve as a valuable supplement to the existing literature by consolidating recent advancements and cutting-edge research in robotic mapping, learning, and collaboration. This perspective encourages researchers to explore integrated solutions and take a more holistic approach to developing intelligent robotic systems. By offering up-to-date and comprehensive insights, this Special Issue pushes the boundaries of robotics, making significant contributions to the field and paving the way for a more technologically advanced and capable future.

Dr. Hilton Tnunay
Guest Editor

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• graph theory
• systems theory
• differential equations and dynamical systems
• nonlinear systems
• robotics
• sensor fusion
• sensor network
• parameter estimation
• mobile mapping
• machine learning
• cooperative control
• mathematical optimisation
• networked system
• human–robot collaboration
• human-in-the-loop control
• automata theory