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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
Mapping Safe Zones for Co-located Human-UAV Interaction
Ayodeji O. Abioye, Lisa Bidgood, Sarvapali D. Ramchurn, Mohammad · 2024-09-03 · via cs.RO updates on arXiv.org

Recent advances in robotics bring us closer to the reality of living, co-habiting, and sharing personal spaces with robots. However, it is not clear how close a co-located robot can be to a human in a shared environment without making the human uncomfortable or anxious. This research aims to map safe and comfortable zones for co-located aerial robots. The objective is to identify the distances at which a drone causes discomfort to a co-located human and to create a map showing no-fly, moderate-fly, and safe-fly zones. We recruited a total of 18 participants and conducted two indoor laboratory experiments, one with a single drone and the other set with two drones. Our results show that multiple drones cause more discomfort when close to a co-located human than a single drone. We observed that distances below 200 cm caused discomfort, the moderate fly zone was 200 - 300 cm, and the safe-fly zone was any distance greater than 300 cm in single drone experiments. The safe zones were pushed further away by 100 cm for the multiple drone experiments. In this paper, we present the preliminary findings on safe-fly zones for multiple drones. Further work would investigate the impact of a higher number of aerial robots, the speed of approach, direction of travel, and noise level on co-located humans, and autonomously develop 3D models of trust zones and safe zones for co-located aerial swarms.