Robotics in Granular Materials: Modeling, Perception, Locomotion and Manipulation (en Inglés) - Zeqing Zhang

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.