分布式WSNs巡检机器人智能定位及实验研究

殷莉; 彭利平; 蔡昌春; 田顺钰

殷莉,彭利平,蔡昌春,田顺钰.分布式WSNs巡检机器人智能定位及实验研究[J].电测与仪表,2026,63(2):73-79.
YiIN Li,PENG Liping,CAI Changchun,TIAN Shunyu.Intelligent positioning and experimental research of inspection robots in distributed WSNs[J].Electrical Measurement & Instrumentation,2026,63(2):73-79.

分布式WSNs巡检机器人智能定位及实验研究

Intelligent positioning and experimental research of inspection robots in distributed WSNs

DOI：10.19753/j.issn1001-1390.2026.02.008

中文关键词: 电力巡检机器人无线网络智能定位非高斯噪声实验分析

英文关键词:Power inspection robot Wireless network Intelligent positioning Non-Gaussian noise Experimental analysis

基金项目:国家自然科学基金面上项目（52371275）；国家重点研发计划项目（2024YFC3211001）； 2024年度江苏省教育科学规划课题（C/2024/02/01）；江苏省输配电重点实验室开放基金（2023JSSPD10)

作者中文名	作者英文名	单位
殷莉	YiIN Li	常州旅游商贸高等职业技术学校
彭利平	PENG Liping	常州旅游商贸高等职业技术学校
蔡昌春	CAI Changchun	河海大学人工智能与自动化学院
田顺钰	TIAN Shunyu	河海大学机电工程学院

摘要点击次数: 74

中文摘要:

无线传感器网络(WSNs)兼具感知和通信的能力，推动了基于目标定位的各类新兴服务应用的发展。考虑到电力巡检机器人通常运行在环境复杂、空间狭小、设备密集的场景中，本文开展了分布式WSNs巡检机器人智能定位及实验评估研究，旨在解决无线通信和测量过程带来较大的定位误差。首先，采用含噪的无线信号达到时间差进行测距，建立无线基站与无线信标间的映射模型；其次，考虑无线测距误差分布推导能够表征巡检机器人分布式位置的完备非线性解析模型；然后，以分布式位置为搜索初值设计了粒子群优化的适应度函数、惯性权重因子和变异操作；最后，从测距噪声和定位精度等方面开展了巡检机器人无线定位的仿真和实验评估。实验结果表明，所提分布式联合粒子群优化的巡检机器人定位算法，相比于其他算法具有更高的定位精度，能够为包括电力装备集群自动化等应用提供技术支持。

英文摘要:

The sensing and communication capabilities of Wireless Sensor Networks (WSNs) promote the development of various emerging service applications based on target positioning. Considering that the power inspection robot usually operates in complex environments, small spaces, and dense equipment, this paper researches the intelligent positioning algorithm and experimental evaluations of distributed WSNs for inspection robots, aiming to solve the problem of large positioning errors in wireless communication and measurement processes. Firstly, a wireless mapping model between wireless base stations and wireless beacons is established with use of noisy time difference of arrival. Secondly, a complete nonlinear analytical model that can characterize the distributed location of inspection robots is derived considering the wireless ranging errors. Then, the fitness function, inertia weight factor, and mutation operation of particle swarm optimization are designed with distributed location as the initial search value. Finally, simulation and experimental evaluations of inspection robot wireless positioning are conducted in terms of ranging noise and positioning accuracy. The experimental results indicate that the proposed distributed joint particle swarm optimization positioning algorithm for inspection robot has higher positioning accuracy compared to other algorithms, and can provide technical support for applications including collaborative automation of power equipment clusters.

查看全文查看/发表评论下载PDF阅读器

关闭