让无人机在工业环境中安全自主地与物体互动
Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers
- 用神经网络+边缘计算实现低延迟目标检测
- 基于控制屏障函数的控制器确保接触时的安全性
- 适合工业场景下的自主无人机系统研发者
本文提出一种新型框架，实现工业场景下无人机的自主物理交互。系统包含两部分：基于神经网络的目标检测模块，结合边缘计算降低机载计算负载；以及基于控制屏障函数（CBF）的控制器，确保无人机安全精确接近目标完成接触。目标检测模型在复杂视觉条件下训练，通过多种未见数据测试，光照变化下仍保持高精度。利用深度特征进行目标位姿估计，整个检测流程由低延迟边缘计算完成。仿真评估了控制器与检测系统的性能，并分析了真实环境下的检测表现。
原文摘要 · Abstract (English)
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.
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