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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
Industrial Cabling in Constrained Environments: a Practic...
Tanureza Jaya, Benjamin Michalak, Marcel Radke, Kevin Haninger · 2024-04-17 · via cs.RO updates on arXiv.org

Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routing, and validation must be solved with one system. This often means the cable must be directly manipulated for routing, and the plug must be manipulated for insertion, often in cluttered environments with tight space constraints. Here we introduce an analysis of the complete industrial cabling tasks and demonstrate a solution from grasp, plug insertion, clipping, and final plug insertion. Industrial requirements are summarized, considering the space limitations, tolerances, and possible ways that the cabling process can be integrated into the production process. This paper proposes gripper designs and general robotic assembly methods for the widely used FASTON and a cubical industrial connector. The proposed methods cover the cable gripping, handling, routing, and inserting processes of the connector. Customized grippers are designed to ensure the reliable gripping of the plugs and the pulling and manipulation of the cable segments. A passive component to correct the cable orientation is proposed, allowing the robot to re-grip the plug before insertion. In general, the proposed method can perform cable assembly with mere position control, foregoing complex control approaches. This solution is demonstrated with an industrial product with realistic space requirements and tolerances, identifying difficult aspects of current cabling scenarios and potential to improve the automation-friendliness in the product design.