分散センシングと超次元計算による見通し外の受動物体検知
Leveraging Distributed Sensing and Hyperdimensional Computing for Non-Line-of-Sight Passive Object Sensing
通信インフラの分散チャネル測定と超次元計算を用い、ロボットの視野外にある人や障害物を高精度に検知する枠組みを提案した。
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著者: Placido Mursia, Marco Rossanese, Francesco Devoti, Adrian Lendinez Ibanez, Lanfranco Zanzi, Xi Li, Xavier Costa-Perez
分類: cs.RO, cs.ET
原文アブストラクト
In smart factory environments, passive entities such as humans and movable obstacles may interfere with autonomous robot operations, posing significant safety risks. Detecting potential conflicts between robots and entities outside their field-of-view (FoV) is therefore of paramount importance. However, conventional passive object detection systems rely on sensors such as LiDAR, radar, or cameras, which operate under direct line-of-sight (LoS) and are limited by obstacles and occlusions. To overcome these limitations, integrated sensing and communication (ISAC) enables environmental perception via the communication infrastructure thanks to cooperation among distributed wireless devices. In this paper, we propose a framework for passive entity sensing beyond the robot's FoV that relies solely on distributed channel measurements from the communication infrastructure to perform risk-aware classification. These measurements are embedded into high-dimensional representations using hyperdimensional computing (HDC), enabling robust and scalable learning without explicit channel reconstruction. Numerical simulations based on a commercial ray tracer and ROS-based validation in realistic smart factory scenarios demonstrate improved safety and enhanced path planning with up to 89% accuracy under severe occlusions, while maintaining low computational complexity.