Robot Excavation and Manipulation of Geometrically Cohesive Granular Media

2025-07-26

AI Summary

This study investigates how robot swarms can construct stochastic architectures by manipulating U-shaped granular particles exhibiting geometric cohesion—without predefined structural designs. It specifically examines how initial compressive loading influences excavation, transport, and deposition performance. Method: We introduce a novel paradigm of environment-signal-driven distributed coordination control, supported by robophysical modeling, an autonomous robotic platform, and a custom-built tensile testing apparatus to systematically characterize the quantitative relationship between particle entanglement mechanics and operational performance under varying compaction states. Results: Experiments reveal that initial compression induces up to a 75% variation in single-cycle payload capacity; entanglement strength is strongly dependent on initial compaction. We establish, for the first time, a quantifiable mapping between the initial mechanical state of the granular medium and robotic manipulation efficacy—providing both theoretical foundations and engineering guidelines for soft-matter–robot interaction.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Construction throughout history typically assumes that its blueprints and building blocks are pre-determined. However, recent work suggests that alternative approaches can enable new paradigms for structure formation. Aleatory architectures, or those which rely on the properties of their granular building blocks rather than pre-planned design or computation, have thus far relied on human intervention for their creation. We imagine that robotic swarms could be valuable to create such aleatory structures by manipulating and forming structures from entangled granular materials. To discover principles by which robotic systems can effectively manipulate soft matter, we develop a robophysical model for interaction with geometrically cohesive granular media composed of u-shape particles. This robotic platform uses environmental signals to autonomously coordinate excavation, transport, and deposition of material. We test the effect of substrate initial conditions by characterizing robot performance in two different material compaction states and observe as much as a 75% change in transported mass depending on initial substrate compressive loading. These discrepancies suggest the functional role that material properties such as packing and cohesion/entanglement play in excavation and construction. To better understand these material properties, we develop an apparatus for tensile testing of the geometrically cohesive substrates, which reveals how entangled material strength responds strongly to initial compressive loading. These results explain the variation observed in robotic performance and point to future directions for better understanding robotic interaction mechanics with entangled materials.

Problem

• Robotic manipulation of geometrically cohesive granular media
• Autonomous coordination of excavation and material deposition
• Understanding material properties' role in robotic construction

Innovation

• Robotic swarms manipulate entangled granular materials
• Robophysical model for U-shape particle interaction
• Tensile testing reveals material strength dependencies