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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
LineMaster Pro: A Low-Cost Intelligent Line Following Rob...
Jeni Shahi, Abhishek Shah, A. S. M. Ahsanul Sarkar Akib · 2026-03-14 · via cs.RO updates on arXiv.org

Line following robots are fundamental platforms in robotics education, yet commercially available solutions remain prohibitively expensive ($150-300$) while lacking integrated obstacle detection capabilities essential for real-world applications. This paper presents LineMaster Pro, an intelligent low-cost line following robot implemented on an Arduino Nano platform that integrates dual TCRT5000 infrared sensors for precision line tracking, an HC-SR04 ultrasonic sensor for real-time obstacle detection, a digitally tuned PID controller with Ziegler-Nichols optimization, and a hierarchical finite state machine for robust obstacle avoidance. A systematic four-phase sensor calibration methodology ensures reliable operation across varying lighting and surface conditions. Experimental validation through 200 controlled trials and 72-hour continuous operation demonstrates mean tracking accuracy of 1.18 cm at 0.4 m/s (95\% CI [1.06, 1.30]), obstacle detection reliability of 96.7\% within 10-40 cm range with 0.7\% false positive rate, and 94\% successful recovery from path deviations. The PID implementation achieves 43\% improvement over conventional on-off control ($p<0.001$). At a total hardware cost of \$28.50 based on verified Bangladesh market prices, LineMaster Pro achieves a 94\% cost reduction compared to commercial alternatives, establishing a practical benchmark for accessible robotics education in resource-constrained environments.