Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1153-1168.doi: 10.13229/j.cnki.jdxbgxb20210038

   

Key technologies in autonomous vehicle for engineering

Xiang-jun YU1(),Yuan-hui HUAI2,Zong-wei YAO3,Zhong-chao SUN1,An YU1()   

  1. 1.School of Mechanical and Electrical Engineering，Kunming University，Kunming 650214，China
    2.Kunming Motor Vehicle Inspection and Supervision Service Center，Kunming 650200，China
    3.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-01-15 Online:2021-07-01 Published:2021-07-14
  • Contact: An YU E-mail:xjykmu@126.com;34018958@qq.com

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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

CLC Number: 

  • TU689

Fig.1

Framework of intelligent earthwork system"

Fig.2

Unmanned load-haul-dump proposed by Atlas"

Fig.3

Schematic diagram of four digital map modeling methods"

Fig.4

Schematic diagram of four popular path planning techniques"

Table 1

Eight path planning algorithms and their characteristics"

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

Table 2

Five types of trajectory tracking algorithms and their characteristics"

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

Fig.5

Six concepts for designing unmanned engineering vehicles"

Fig.6

Schematic of multi machine tele-remote system proposed by Atlas"

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