智能化挖掘机的研究现状与发展趋势

doi:10.3901/JME.2020.13.165

摘要/Abstract

摘要： 智能化挖掘机是传统挖掘机与人工智能、自动控制和信息物理网络等技术深度融合的产物。相对于传统挖掘机，其拥有更高的功率利用率及作业精度，集远程作业、环境感知、智能诊断为一体，在抗震救灾、太空及水下作业等领域都有广阔的应用前景。然而挖掘机液压系统具有强非线性、流量耦合、时变等特点，而工作装置又存在动力学耦合和负载不确定性，结构简单、参数依赖度低、高精度的智能控制算法将提高挖掘机自主作业精度；由于视觉传感器易受光照、气象条件影响，研究多传感器融合及其智能算法将提升挖掘机的环境感知能力；液压系统故障模式隐藏性高，受机载设备硬件限制，探索非冗余小规模深度学习网络和压缩感知技术是实现在线智能故障诊断的关键。从轨迹控制、环境感知、远程控制与智能故障诊断四个方面，综述了挖掘机智能化的国内外研究现状；指出了智能化挖掘机发展存在的问题与发展趋势；最后根据现有研究成果得出了五点结论。

关键词: 智能化挖掘机, 轨迹控制, 环境感知, 远程控制, 故障诊断

Abstract: The intelligent excavator is the transformation and upgrading of the traditional excavator by using artificial intelligence, automatic control and cyber-physical technology, which has higher energy efficiency and operation precision. Because of the remote operation, environment perception and fault diagnosis, it has wide potential application in earthquake relief, space and underwater operations. However, the excavator hydraulic system has the characteristics of powerful nonlinearity time-varying, and strong fluid coupling, while the working device has dynamic coupling and load uncertainty. Therefore, the intelligent control algorithm with simple structure, low parameter dependence and high precision is the basis of the autonomous excavation. The visual sensor is easily affected by illumination and meteorological conditions. Thus the Multi-sensor fusion intelligent algorithm will enhance the environmental sensing ability of the excavator. The fault mechanisms of the hydraulic system is highly complex and diverse. Because of the limit of the hardware, the non-redundant small-scale deep learning network and compressed-sensing technology is the key to realizing online fault diagnosis. The inland and overseas research status of intelligent excavators in trajectory control, environment perception, remote control and fault diagnosis are reviewed. Meanwhile, the existing key problems and development trend of the research on intelligent excavators are pointed out. At last, five conclusions about the current research status are drawn.

Key words: intelligent excavator, trajectory control, environment perception, remote control, fault diagnosis

中图分类号:

TU621

李运华, 范茹军, 杨丽曼, 赵斌, 权龙. 智能化挖掘机的研究现状与发展趋势[J]. 机械工程学报, 2020, 56(13): 165-178.

LI Yunhua, FAN Rujun, YANG Liman, ZHAO Bin, QUAN Long. Research Status and Development Trend of Intelligent Excavators[J]. Journal of Mechanical Engineering, 2020, 56(13): 165-178.

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