Buch, Englisch, 436 Seiten, Format (B × H): 155 mm x 235 mm
Proceedings of the 19th International Conference IAS-19, Volume 2
Buch, Englisch, 436 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-33870-9
Verlag: Springer Nature Switzerland AG
This book features the proceedings of the 19th International Conference on Intelligent Autonomous Systems (IAS-19), held in Genoa, Italy, from June 30 to July 4, 2025. Intelligent autonomous systems are playing an increasingly vital role across a wide range of domains, from industrial settings and professional services to everyday household applications. As these technologies evolve, they bring new challenges that demand ongoing research and development to ensure their deployment is reliable, efficient, and independent of user intervention.
This book showcases recent advancements in the field, aiming to support both researchers and practitioners by offering a timely collection of innovative work. With the theme “Ethical, Responsible, and Inclusive Robotics,” the conference brought together discussions and insights on cutting-edge theories, practical applications, and creative innovations in intelligent autonomous systems. Topics covered include human-robot interaction, machine learning and adaptation, path planning and navigation, perception and mapping, field and marine robotics, and humanoids and robotic manipulators.
The articles included in this book serve as a valuable resource for scholars and professionals seeking to deepen their understanding of intelligent autonomous systems.
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Research
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Weitere Infos & Material
Detecting Adversarial Interference In Cooperative Tasks Through Multi dimensional Bayesian Trust Metric.- CROMU Climate Resilience by Operating Multi UAS.- On the Fly Adaptation of Behavior Tree Based Policies through Reinforcement Learning.- Dual Arm Cooperative Cable suspended Manipulation Using Multi Agent Reinforcement Learning.- Beyond Predefined Actions Integrating Behavior Trees and Dynamic Movement Primitives for Robot Learning from Demonstration.
