Paper Reading AI Learner

Whisker-based Tactile Flight for Tiny Drones

2025-10-03 15:47:50
Chaoxiang Ye, Guido de Croon, Salua Hamaza

Abstract

Tiny flying robots hold great potential for search-and-rescue, safety inspections, and environmental monitoring, but their small size limits conventional sensing-especially with poor-lighting, smoke, dust or reflective obstacles. Inspired by nature, we propose a lightweight, 3.2-gram, whisker-based tactile sensing apparatus for tiny drones, enabling them to navigate and explore through gentle physical interaction. Just as rats and moles use whiskers to perceive surroundings, our system equips drones with tactile perception in flight, allowing obstacle sensing even in pitch-dark conditions. The apparatus uses barometer-based whisker sensors to detect obstacle locations while minimising destabilisation. To address sensor noise and drift, we develop a tactile depth estimation method achieving sub-6 mm accuracy. This enables drones to navigate, contour obstacles, and explore confined spaces solely through touch-even in total darkness along both soft and rigid surfaces. Running fully onboard a 192-KB RAM microcontroller, the system supports autonomous tactile flight and is validated in both simulation and real-world tests. Our bio-inspired approach redefines vision-free navigation, opening new possibilities for micro aerial vehicles in extreme environments.

Abstract (translated)

小型飞行机器人在搜索救援、安全检查和环境监测方面具有巨大潜力,但其小尺寸限制了传统传感器的使用,特别是在光线昏暗、烟雾、尘埃或反射性障碍物的情况下。受自然启发,我们提出了一种轻量级、仅重3.2克、基于触须的触觉传感装置,专为微型无人机设计,使它们能够通过温和的身体互动导航和探索。就像老鼠和鼹鼠使用胡须来感知周围环境一样,我们的系统让无人机在飞行中具备了触觉感知能力,在完全黑暗的情况下也能进行障碍物检测。 该装置采用基于气压的触须传感器来探测障碍物的位置,同时尽量减少对无人机稳定性的干扰。为了解决传感器噪声和漂移问题,我们开发了一种触觉深度估计方法,能够实现亚6毫米的精度。这使得无人机能够在完全黑暗中通过触摸导航、绕过障碍物,并探索狭窄的空间——无论是柔软还是坚硬表面。 整个系统在仅配备192KB RAM的微控制器上运行,支持自主触控飞行,并已在模拟和现实世界测试中得到了验证。我们的仿生方法重新定义了无视觉导航的可能性,为微型飞行器在极端环境中的应用开辟了新的可能性。

URL

https://arxiv.org/abs/2510.03119

PDF

https://arxiv.org/pdf/2510.03119.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot