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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
The effects of increasing velocity on the tractive perfor...
David Rodríguez-Martínez, Fabian Buse, Michel Van Winnendael, Ka · 2023-06-04 · via cs.RO updates on arXiv.org

An emerging paradigm is being embraced in the conceptualization of future planetary exploration missions. Ambitious objectives and increasingly demanding mission constraints stress the importance associated with faster surface mobility. Driving speeds approaching or surpassing 1 m/s have been rarely used and their effect on performance is today unclear. This study presents experimental evidence and preliminary observations on the impact that increasing velocity has on the tractive performance of planetary rovers. Single-wheel driving tests were conducted using two different metallic, grousered wheels-one rigid and one flexible-over two different soils, olivine sand and CaCO3-based silty soil. Experiments were conducted at speeds between 0.01-1 m/s throughout an ample range of slip ratios (5-90%). Three performance metrics were evaluated: drawbar pull coefficient, wheel sinkage, and tractive efficiency. Results showed similar data trends among all the cases investigated. Drawbar pull and tractive efficiency considerably decreased for speeds beyond 0.2 m/s. Wheel sinkage, unlike what published evidence suggested, increased with increasing velocities. The flexible wheel performed the best at 1m/s, exhibiting 2 times higher drawbar pull and efficiency with 18% lower sinkage under low slip conditions. Although similar data trends were obtained, a different wheel-soil interactive behavior was observed when driving over the different soils. Overall, despite the performance reduction experienced at higher velocities, a speed in the range of 0.2-0.3 m/s would enable 5-10 times faster traverses, compared to current rovers driving capability, while only diminishing drawbar pull and efficiency by 7%. The measurements collected and the analysis presented here lay the groundwork for initial stages in the development of new locomotion subsystems for planetary surface exploration. At the same time...