High-Precision, High-Speed, and Low-Computing-Power AUV Optical Positioning Method

XIE Zhengbin; SUN Zhe; ZHAN Xufeng; LI Xuelong

doi:10.12263/DZXB.20250753

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High-Precision, High-Speed, and Low-Computing-Power AUV Optical Positioning Method

PAPERS | 更新时间：2026-06-22

- High-Precision, High-Speed, and Low-Computing-Power AUV Optical Positioning Method
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 141-152(2026)
- 作者机构：
  
  1.西北工业大学光电与智能研究院，陕西西安 710072
  2.中国电信人工智能研究院（TeleAI），上海 200030
  3.中国科学技术大学信息科学与技术学院，安徽合肥 230026
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFC2808003);Natural Science Basic Research Program of Shaanxi(2024JC-YBMS-468)
- DOI：10.12263/DZXB.20250753
  CLC： TP242.3;
- Received：26 August 2025，
  
  Accepted：07 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
谢正斌, 孙哲, 战绪丰, 等. 高精度高速度低算力的AUV水下导引光学定位方法[J]. 电子学报, 2026, 54(01): 141-152.

XIE Zhengbin, SUN Zhe, ZHAN Xufeng, et al. High,-Precision, High,-Speed, and Low,-Computing,-Power AUV Optical Positioning Method[J]. Acta Electronica Sinica, 2026, 54(01): 141-152.
谢正斌, 孙哲, 战绪丰, 等. 高精度高速度低算力的AUV水下导引光学定位方法[J]. 电子学报, 2026, 54(01): 141-152. DOI：10.12263/DZXB.20250753

XIE Zhengbin, SUN Zhe, ZHAN Xufeng, et al. High,-Precision, High,-Speed, and Low,-Computing,-Power AUV Optical Positioning Method[J]. Acta Electronica Sinica, 2026, 54(01): 141-152. DOI：10.12263/DZXB.20250753

摘要

自主水下航行器（Autonomous Underwater Vehicles，AUV）在长时间执行海洋探测及其他作业任务后，必须回到回收站进行能源补充和数据传输。在AUV的末端回收阶段，其定位系统的定位精度与定位速度直接影响AUV的导引成功率。目前的导引技术中，声学导引方法虽然作用距离较远，但其定位精度难以满足近距离对接要求；基于视觉的导引方法虽然精度较高，但其易受水体浑浊、光照散射等外部环境因素干扰，且该方法涉及复杂的图像特征提取与矩阵运算，对AUV所承载的计算平台算力和功耗提出了更高的要求。针对AUV算力受限以及传统视觉方法实时性差、计算量大的问题，本文提出了一种软硬件一体化的轻量高速光学定位方案。本研究构建了基于多象限光电探测器的AUV导引模型。在硬件方面，本方案是用搭载于AUV前端的8 × 8阵列式多象限面阵探测器作为信号接收端，回收站前方部有正三角形排布的LED（Light-Emitting Diode）三导引灯组作为信号发射端。该探测器通过测量入射光斑的形心位置计算三组光信号的入射偏角，避免了传统视觉系统中大量的图像矩阵计算。在数学模型上，本文建立了从偏角信息到相对空间坐标的映射关系。考虑到AUV的翻滚角在结构设计阶段已得到约束，本模型去掉了翻滚角的信息，有效减少了因姿态测量误差导致的定位精度下降问题，提升了系统的鲁棒性。针对空间中的非线性求解问题，本文引入了改进粒子群优化算法（Particle Swarm Optimization，PSO），以最小化预测偏角与实际测量偏角的误差和作为损失函数，实现了AUV相对位姿的快速估计。为了验证本算法性能，本文开展了物理仿真与海试验证。首先，基于物理模型生成了包含10万组不同数据的仿真数据集，其中涵盖0~20 m内的不同距离与姿态信息。随后，将算法部署于低功耗边缘计算平台Jetson Orin NX进行实测。实验结果表明，在速度方面，本系统可以在192 Hz的频率下稳定解算AUV的位姿信息；在精度方面，在0.6~2 m的末端导引距离内，本算法的平均定位误差仅为7.81 mm；在2~20 m的中远导引距离内，平均定位误差为159.90 mm。此外，基于ROV（Remotely Operated Vehicle）的海试实验中，本文以GPS（Global Positioning System）数据作为基准真值，扣除硬件基准误差后，与仿真实验的精度水平基本一致，进一步说明了算法在真实水下环境中的鲁棒性和高效性。相比于已有的视觉导引方法，本方法在保证毫米级导引定位精度的同时，展现出了一定的计算量和功耗优势。本算法的单次解算浮点运算量降低至1 MFLOPs（Million FLoating-point Operations Per second），相比文中所列其他方法减少了2~3个数量级，在Jetson Orin NX上的运行功耗仅约为10 W。本研究进一步缓解了AUV水下末端导引中高精度、高速度与低算力要求之间的矛盾，为边缘型水下机器人的高效自主回收提供了新的思路。

Abstract

After performing long-duration ocean exploration and other operational tasks

autonomous underwater vehicles (AUV) must return to a recovery station for energy replenishment and data transmission. During the AUV’s terminal recovery phase

the positioning accuracy and speed of its positioning system directly influence the success rate of AUV guidance. Among current guidance technologies

acoustic guidance methods offer long operating ranges but their positioning accuracy struggles to meet close-range docking requirements; while vision-based guidance methods offer higher accuracy

they are susceptible to interference from external environmental factors such as water turbidity and light scattering. Furthermore

such methods involve complex image feature extraction and matrix operations

placing higher demands on the computing power and power consumption of the computational platform carried by the AUV. Addressing the issues of limited AUV computing power

poor real-time performance of traditional visual methods

and high computational load

this paper proposes a hardware-software integrated lightweight high-speed optical positioning scheme. This study constructs an AUV guidance model based on a multi-quadrant photoelectric detector. In terms of hardware

this scheme uses an 8 × 8 array multi-quadrant area detector mounted on the front of the AUV as the signal receiver

with a group of three light-emitting diode (LED) guidance lights arranged in an equilateral triangle deployed at the front of the recovery station as the signal transmitter. The detector calculates the incident deviation angles of the three optical signals by measuring the centroid position of the incident light spots

avoiding the massive image matrix calculations of traditional visual systems. In the mathematical model

this paper establishes the mapping relationship from angular deviation information to relative spatial coordinates. Considering that the AUV’s roll angle is constrained during the structural design phase

information regarding the roll angle is removed from this model

effectively reducing positioning accuracy degradation caused by attitude measurement errors and enhancing system robustness. Addressing the non-linear solving problem in space

this paper introduces an improved particle swarm optimization (PSO) algorithm

using the sum of errors between the predicted deviation angles and the actual measured deviation angles as the loss function

achieving rapid estimation of the AUV’s relative pose. To verify the performance of this algorithm

this paper conducted physical simulations and sea trial validations. First

based on a physical model

a simulation dataset containing 100 000 sets of different data was generated

covering different distance and attitude information within 0 m to 20 m. Subsequently

the algorithm was deployed on the low-power edge computing platform Jetson Orin NX for actual testing. Experimental results show that in terms of speed

the system can stably solve for the AUV’s pose information at a frequency of 192 Hz; in terms of accuracy

within the terminal guidance distance of 0.6 m to 2 m

the average positioning error of this algorithm is only 7.81 mm; within the medium-to-long guidance distance of 2 m to 20 m

the average positioning error is 159.90 mm. Furthermore

in sea trial experiments based on a remotely operated vehicle (ROV)

this paper used global positioning system (GPS) data as the ground truth benchmark. After deducting hardware baseline errors

the accuracy level of the simulation experiments was maintained

further illustrating the robustness and efficiency of the algorithm in a real underwater environment. Compared with existing vision-based guidance methods

this method demonstrates specific advantages in computational load and power consumption while ensuring millimeter-level guidance positioning accuracy: the floating-point operations for a single solution of this algorithm are reduced to 1 million floating-point operations per second (MFLOPs)

a decrease of 2 to 3 orders of magnitude compared to other methods listed in the paper

and the operating power consumption on the Jetson Orin NX is only about 10 W. This research further alleviates the contradiction between the requirements for high accuracy

high speed

and low computing power in the underwater terminal guidance of AUVs

providing a new approach for the efficient autonomous docking of edge-type underwater robots.

关键词

Keywords

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