工程车辆无人驾驶关键技术

doi:10.13229/j.cnki.jdxbgxb20210038

摘要/Abstract

摘要：

随着社会对工程车辆操作人员生命安全的重视以及工程项目对施工机械效能要求标准的提高，工程车辆正向高自主、高效率、高可靠性方向发展。为实现工程车辆无人驾驶自动转场及作业，本文系统总结了国内外相关技术，从环境感知、运动规划、工程作业、状态监测等方面详细分析了工程车辆无人驾驶关键技术的研究进展，指出非结构环境识别、车身可变结构车辆的路径规划及轨迹跟踪、自动化作业等方面的技术尚需突破，提出采用机构/结构优化设计、先进的通讯手段、机器学习和数字孪生等方法，有利于推动工程车辆无人驾驶关键技术发展。

关键词: 工程车辆, 无人驾驶, 环境感知, 运动规划, 数字孪生

Abstract:

As the society emphasizes on the life safety of operators and the standard of machinery performance requirements for construction， engineering vehicles are developing in the direction of autonomy， efficiency and reliability. In order to realize the automatic transfer and operation of unmanned engineering vehicles， this paper systematically summarizes the relevant technologies at home and abroad， and analyzes the research progress of key technologies of unmanned engineering vehicles in detail in terms of environment perception， motion planning， engineering operation and condition monitoring， etc. It points out that the technologies of unstructured environment identification， path planning and trajectory tracking of vehicles with variable body structure and automated operation still need to be broken through， and proposes the adoption of mechanism/structure optimization design， advanced communication means， machine learning and digital twin， etc.， which is conducive to promoting the development of key technologies of unmanned engineering vehicles.

Key words: engineering vehicles, unmanned driving, environment perception, motion planning, digital twin

中图分类号:

TU689

于向军,槐元辉,姚宗伟,孙中朝,俞安. 工程车辆无人驾驶关键技术[J]. 吉林大学学报(工学版), 2021, 51(4): 1153-1168.

Xiang-jun YU,Yuan-hui HUAI,Zong-wei YAO,Zhong-chao SUN,An YU. Key technologies in autonomous vehicle for engineering[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1153-1168.

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算法名称	核心思路	主要优点	主要缺点
可视图法	规划问题转化为图论问题	概念清晰，易于实现，多用于二维地图	缺乏灵活性，无法用于三维规划
栅格图法	地图栅格化	简单直观，可结合多类搜索算法进行规划	划分粒度影响计算，无法处理动态障碍物
人工势场法	构建虚拟人工势场，模拟引斥力	结构简单，实时性高，计算量小	障碍物分布位置影响结果
模糊逻辑法	基于模糊逻辑推理模型	环境未知或发生变化时，可快速准确规划出路径	障碍物数目增加影响计算速度
遗传算法	模拟生物遗传时的选择、交叉和变异	多点搜索，理论上可以得到全局最优解	运行速度满，实际操作时会产生“早熟”收敛
神经网络算法	模拟神经元网络结构与特征	并列性算法，可达任意精度，可实现二、三维规划	只适用于环境已知且障碍物静止
蚁群算法	模拟自然界蚂蚁觅食机制	分布式并行计算，效率高，不会陷入局部极优值	前期速度慢，参数调节依赖于经验
RRT算法	随机采样的方式生长节点	无需对环境建模，可解决三维及更高维度的复杂约束问题	生成路径质量低，不光滑，通常远离最优路径

算法名称	核心思路	主要优点	主要缺点
PID控制	根据系统误差，利用比例、积分、微分计算控制量	算法简单，鲁棒性强，可靠性高，尤其适用于确定性控制系统	控制器参数整定不良，对工况适应能力差
滑模控制	系统按照预定“滑动模态”的状态轨迹有目的地改动当前状态	响应快速，鲁棒性强，无需系统辨识，物理实现简单	状态轨迹难以严格沿滑动模态面向平衡点滑动，在平衡点附近产生抖动
模型预测控制	在每一个采样瞬间求解一个有限时域开环最优控制问题，获得当前控制动作	建模方便，鲁棒性强，动态控制性能好，能有效处理多变量、有约束问题	难以获得设计参数与动静态特性的解析关系，难以解决不确定性系统问题
模糊控制	利用模糊数学的基本思想和理论实现对系统的精确控制	简化系统设计复杂性，不依赖精确数学模型，控制器不必对被控对象建立完整的数学模式	完全凭经验建立模糊规则及隶属函数，精度与决策速度相矛盾，鲁棒性差
神经网络控制	人工神经元与控制理论相结合，模拟人类智能	融合其他控制算法解决非线性、不确定、不确知系统的控制问题	学习速度慢，稳定性、收敛性差

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