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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Traction of Interlocking Spikes on a Granular Material
Volker Nannen, Damian Bover · 2022-04-03 · via cs.RO updates on arXiv.org

The interlock drive system generates traction by inserting narrow articulated spikes into the ground and by leveraging the soil's strength to resist horizontal draft forces. The system promises high tractive performance in low gravity environments where tires have little traction for lack of weight. At Earth and Space 2021 we reported the performance of such spikes on a silty clay loam, a cohesive soil. We found that in such soil, traction below a critical depth is provided by a zone of lateral soil failure. We also found that the articulation translates a horizontal draft force into a vertical penetration force strong enough to penetrate a narrow spike to a depth where the soil can sustain the draft force, in a self-regulating way. It is conceivable that a granular material like regolith or sand with little to no cohesive strength provides less vertical penetration resistance and less resistance to a horizontal draft force than a cohesive soil, which leads to the question of whether and how much tractive force an interlocking spike can generate on a granular material. Here we report on field trials that study different spike designs in dry and unsaturated moist sand. The results demonstrate that a loose granular material requires larger spikes than a cohesive soil, that these larger spikes penetrate dry and moist sand reliably, and that they promise good tractive efficiency. The trials indicate that on sand, a larger spike diameter can improve the pull/weight ratio without a loss of tractive performance.