惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
MongoDB | Blog
MongoDB | Blog
博客园_首页
博客园 - 三生石上(FineUI控件)
博客园 - 聂微东
B
Blog RSS Feed
D
Docker
IT之家
IT之家
大猫的无限游戏
大猫的无限游戏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
阮一峰的网络日志
阮一峰的网络日志
罗磊的独立博客
Recent Announcements
Recent Announcements
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
A
About on SuperTechFans
The GitHub Blog
The GitHub Blog
G
Google Developers Blog
V
V2EX
量子位
雷峰网
雷峰网
月光博客
月光博客
云风的 BLOG
云风的 BLOG
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
T
Tailwind CSS Blog

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
Two-Layer Voronoi Coverage Control for Hybrid Aerial-Grou...
Douglas Hutchings, Luai Abuelsamen, Karthik Rajgopal · 2025-10-13 · via cs.RO updates on arXiv.org

We present a comprehensive two-layer Voronoi coverage control approach for coordinating hybrid aerial-ground robot teams in hazardous material emergency response scenarios. Traditional Voronoi coverage control methods face three critical limitations in emergency contexts: heterogeneous agent capabilities with vastly different velocities, clustered initial deployment configurations, and urgent time constraints requiring rapid response rather than eventual convergence. Our method addresses these challenges through a decoupled two-layer architecture that separately optimizes aerial and ground robot positioning, with aerial agents delivering ground sensors via airdrop to high-priority locations. We provide detailed implementation of bounded Voronoi cell computation, efficient numerical integration techniques for importance-weighted centroids, and robust control strategies that prevent agent trapping. Simulation results demonstrate an 88% reduction in response time, achieving target sensor coverage (18.5% of initial sensor loss) in 25 seconds compared to 220 seconds for ground-only deployment. Complete implementation code is available at https://github.com/dHutchings/ME292B.