Robotics in Granular Materials

Modeling, Perception, Locomotion and Manipulation
Erscheinungsjahr 2027
ISBN: 978-981-9255-28-3
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Granular materials such as sand, soil, gravel, regolith, powders, and grains are central to planetary surfaces, agricultural fields, construction sites, mines, food-processing systems, and disaster-response environments. For robots, these media are both common and unusually difficult: they deform, compact, flow, jam, collapse, and shift between solid-like and fluid-like behavior. Understanding how robots interact with granular environments is therefore essential for advancing autonomy beyond structured and predictable settings.

This book provides a systematic review of robotics in and around granular media, organized around a four-pillar framework that connects modeling, sensing, locomotion, and manipulation. It introduces key foundations including terradynamics, resistive force theory, discrete element methods, continuum modeling, multi-body simulation, differentiable physics, and data-driven simulators. It then examines subsurface sensing, media identification, buried object localization, wheeled and tracked mobility, legged locomotion, burrowing, hybrid mechanisms, excavation, retrieval, granular handling, and task-oriented manipulation. The final parts synthesize dominant methodologies, identify bottlenecks in scalability, standardization, real-time control, and sim-to-real transfer, and outline future directions such as physics-grounded AI models, adaptive autonomy, multi-fidelity simulation, neuromorphic sensing, and shared benchmarking protocols.

The book is intended for researchers, engineers, graduate students, and practitioners working in robotics, granular physics, terramechanics, field robotics, planetary exploration, agricultural automation, construction robotics, disaster response, and industrial material handling. By combining foundational theory with recent robotic systems and computational methods, it offers both a rigorous reference and a forward-looking roadmap for those seeking to understand and advance robotic autonomy in complex granular environments.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Zeqing

Zhou, Peng

Pan, Jia

Yang, Chenguang

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Naturwissenschaften Physik Thermodynamik Festkörperphysik, Kondensierte Materie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Chapter 1 Background, Introduction and Motivation.- Chapter 2 Fundamental Physics and Modeling Approaches.- Chapter 3 Sensing and Perception in Granular Media.- Chapter 4 Locomotion on and in Granular Terrain.- Chapter 5 Manipulation and Handling of Granular Materials.- Chapter 6 Comparative Methodology Analysis.- Chapter 7 Critical Gaps and Technical Bottlenecks.- Chapter 8 Future Directions and Emerging Paradigms.- Chapter 9 Conclusion.

Über Autor(innen)

Zeqing Zhang (Member, IEEE) received the Ph.D. degree in computer science from The University of Hong Kong in 2024. He is currently a Research Fellow at Nanyang Technological University. His research focuses on robotic perception and manipulation of soft bodies and granular media. He has published as first author or corresponding author in leading robotics venues, including one ESI Highly Cited Paper. He serves as an Associate Editor for IEEE Robotics and Automation Letters and IEEE/RSJ IROS 2026. He received the Best Conference Paper Award Finalist at IEEE ROBIO 2025 and Best Poster Award in IEEE ICRA 2026 workshop.

Peng Zhou (Member, IEEE) received the Ph.D. degree in robotics from The Hong Kong Polytechnic University in 2022. He was a Visiting Researcher at the Robotics, Perception, and Learning Lab, KTH Royal Institute of Technology, Stockholm, Sweden, in 2021, and a Postdoctoral Research Fellow at the Department of Computer Science, The University of Hong Kong, from 2022 to 2024. He is currently an Assistant Professor in the School of Advanced Engineering, Great Bay University. His research interests include robot manipulation, robot learning, and task and motion planning. He serves as an Associate Editor of IEEE Robotics and Automation Letters and Frontiers in Robotics and AI.

Jia Pan (Senior Member, IEEE) received the Ph.D. degree in computer science from the University of North Carolina at Chapel Hill, USA, in 2013. He is currently an Associate Professor in the Department of Computer Science, The University of Hong Kong, and Chief Scientist of LimX Dynamics. His research interests include robotics and artificial intelligence for autonomous systems, especially navigation and manipulation in challenging tasks, such as movement in dense human crowds and deformable-object manipulation for garment automation. He serves as an Associate Editor for IEEE Transactions on Robotics.

Chenguang Yang (Fellow, IEEE) received the Ph.D. degree in control engineering from the National University of Singapore in 2010. He is currently a Professor in the Department of Computing, The Hong Kong Polytechnic University. His research interests include robot control and learning, human-robot interaction, and intelligent system design. He is a Member of the European Academy of Sciences and Arts and has received the UK EPSRC UKRI Innovation Fellowship and the EU Marie Curie International Incoming Fellowship. He is the Corresponding Co-Chair of the IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing. As lead author, he received the IEEE Transactions on Robotics Best Paper Award in 2012 and the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022.