参数不确定性优化方法在转向检测与校正系统中的应用研究

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jiahao Yang;Ming Xu;Longhua Ma;Fangle Chang;Wenxiang Wu
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引用次数: 0

摘要

本文介绍了一种新颖的航向角检测和补偿方法,旨在解决无人机器人在日常检查工作中遇到的导航和定位精度难题,从而大大提高智能港口建设的标准,促进高质量港口的发展。扩展卡尔曼滤波(EKF)算法和全球导航卫星系统(GNSS)惯性导航系统(INS)/磁力计组合导航技术构成了这一战略的基础。与传统的 EKF 算法相比,建议的偏差检测和补偿方法大大提高了导航系统的性能。此外,我们还通过添加粒子群优化(PSO)算法,提高了导航系统适应复杂环境和突发变化的能力。这使我们能够在原始创新的基础上进一步优化系统参数。这一研发成果对于提高智能港口无人机器人导航精度至关重要,并为港口自动化和智能化的发展提供了强有力的技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application Research of Parameter Uncertainty Optimization Method in Steering Detection and Correction System
A novel heading angle detection and compensation method is presented with the aim of addressing the navigation and localization accuracy challenges that unmanned robots encounter in their daily inspection jobs, thereby significantly raising the bar for smart port building and promoting the development of ports of superior quality. The Extended Kalman Filter (EKF) algorithm and a Global Navigation Satellite System (GNSS) Inertial Navigation System (INS)/Magnetometer combination navigation technology form the basis of this strategy. The suggested deviation detection and compensating method greatly enhances the navigation system’s performance when compared to the conventional EKF algorithm. Furthermore, we improved the navigation system’s ability to adapt to complex surroundings and sudden changes by adding the Particle Swarm Optimization (PSO) algorithm to the process. This allowed us to further optimize the system parameters based on the original innovation. This development is critical to enhancing unmanned robot navigation accuracy at smart ports and providing robust technical support for the growth of port automation and intelligence.
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CiteScore
5.70
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