摘要
为了满足日益增长的电力需求,我国建设了大量发电站和电能转换设施。长时间的负载运行使电力设备运行的不稳定性增加,一般需要通过人工巡检的方式进行检测和维护,但传统的人工巡检方法存在着较大的安全风险,在精度方面满足不了需求,维护效率也比较低。随着人工智能技术的发展,智能巡检机器人的出现给这一问题带来希望,它克服了传统人工巡检方式存在的弊端,但目前的巡检机器人在复杂电力环境中仍存在空间定位不准确等问题。为此,本文提出了一种将超宽带(UWB)和惯性传感器(IMU)以无迹卡尔曼滤波(UKF)进行融合的定位方法,该方法克服了UWB定位方法在有障碍物的非视距情况下定位误差大的问题,改进了复杂电力环境下智能巡检机器人的定位精度。
In order to meet the growing demand for electric power, China has built a large number of power stations and electric energy conversion facilities. The long-term load operation increases the instability of power equipment operation. Electric facilities are generally inspected and maintained by manual inspection. However, the traditional manual inspection method has security risk, which cannot meet the demand in terms of accuracy, and the maintenance efficiency is also relatively low. With the development of artificial intelligence technology, the emergence of intelligent inspection robot brings hope to this problem. It overcomes the aforementioned disadvantages of traditional manual inspection methods. However, the current inspection robot still has some problems such as inaccurate positioning in the complex power environment. Therefore, this paper proposes a positioning method based on the fusion of UWB and IMU with UKF, and selects the observed values in the model through the residual method. This positioning method of electric inspection robot overcomes the problem of large positioning error of UWB positioning method in the case of non-line of sight, and improves the positioning accuracy of intelligent inspection robot in complex electric environment.
出处
《自动化博览》
2022年第4期46-51,共6页
Automation Panorama1
基金
杭州电力设备制造有限公司科技项目(YF211601)。
关键词
超宽带定位
IMU惯性导航
非视距识别
非视距识别误差抑制
无迹卡尔曼滤波
UWB wideband localization
IMU inertial navigation
NLOS recognition error suppression
NLOS error mitigation
Unscented Kalman filter
