信頼度適応型マルチモーダル相対姿勢追跡による水中リーダー・フォロワーロボット制御
Reliability-Adaptive Multimodal Relative Pose Tracking for Underwater Leader-Follower Robots
水中の2台のロボット間の6自由度相対姿勢を、視覚・音響測距・流体力のマルチモーダル計測を信頼度に応じて適応的に重み付けして推定する手法を提案し、AUVによる648回のプール実験で平均3.8cm・2.4°の精度と組込み機器での高速推論を実証した。
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著者: Mingyang Yang, Zeyu Sha, Xiaorui Wang, Jun Wang, David Chen Wang, Feitian Zhang
分類: cs.RO
原文アブストラクト
Underwater leader-follower formation control requires continuous relative-pose estimation despite degraded sensing conditions and limited onboard computation. This paper presents a reliability-adaptive multimodal perception framework for six-degree-of-freedom relative-pose tracking between two moving underwater robots. The framework integrates complementary visual, acoustic-ranging, and hydrodynamic measurements while accounting for their distinct physical characteristics and time-varying reliability. It adaptively weights modality contributions and models temporal dependencies with linear computational complexity, enabling efficient pose estimation on embedded hardware. Extensive experiments were conducted using a custom-built AUV platform, with 648 pool trials spanning two leader vehicle types, three illumination conditions, and nine trajectory patterns. Evaluation yields an average translational error of 3.8 cm and an average orientation error of 2.4°. On an onboard NVIDIA Jetson AGX Orin, the fusion network achieves an inference latency of 17.62 ms, providing a 2.69-fold speedup over the evaluated Transformer-based fusion baseline. Closed-loop experiments further demonstrate stable formation maintenance with a moving leader under onboard computational constraints. A multimodal dataset containing measurements from all 648 trials will be made publicly available.