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保证智能轮椅平滑通过狭窄通道的路径曲率优化算法

陈灵 王森 胡豁生 麦当劳-麦尔·克劳斯 费敏锐

陈灵, 王森, 胡豁生, 麦当劳-麦尔·克劳斯, 费敏锐. 保证智能轮椅平滑通过狭窄通道的路径曲率优化算法. 自动化学报, 2016, 42(12): 1874-1885. doi: 10.16383/j.aas.2016.c160185
引用本文: 陈灵, 王森, 胡豁生, 麦当劳-麦尔·克劳斯, 费敏锐. 保证智能轮椅平滑通过狭窄通道的路径曲率优化算法. 自动化学报, 2016, 42(12): 1874-1885. doi: 10.16383/j.aas.2016.c160185
CHEN Ling, WANG Sen, HU Huo-Sheng, MCDONALD-MAIER Klaus, FEI Min-Rui. Novel Path Curvature Optimization Algorithm for Intelligent Wheelchair to Smoothly Pass a Narrow Space. ACTA AUTOMATICA SINICA, 2016, 42(12): 1874-1885. doi: 10.16383/j.aas.2016.c160185
Citation: CHEN Ling, WANG Sen, HU Huo-Sheng, MCDONALD-MAIER Klaus, FEI Min-Rui. Novel Path Curvature Optimization Algorithm for Intelligent Wheelchair to Smoothly Pass a Narrow Space. ACTA AUTOMATICA SINICA, 2016, 42(12): 1874-1885. doi: 10.16383/j.aas.2016.c160185

保证智能轮椅平滑通过狭窄通道的路径曲率优化算法

doi: 10.16383/j.aas.2016.c160185
基金项目: 

上海市科委重点项目 14JC1402200

上海市科委重点项目 15411953502

上海市科委扬帆人才计划项目 16YF1403700

上海高校青年教师培养资助计划 ZZSD15088

详细信息
    作者简介:

    王森 英国牛津大学计算机科学学院助理研究员.主要研究方向为机器人定位和多传感器融合.E-mail:sen.wang@cs.ox.ac.uk

    胡豁生 英国埃塞克斯大学计算机科学与电子系统学院教授, 机器人实验室主任.主要研究方向为自主机器人和网络化机器人.E-mail:hhu@essex.ac.uk

    麦当劳-麦尔·克劳斯 英国埃塞克斯大学计算机科学与电子系统学院教授.主要研究方向为嵌入式系统和机器人应用.E-mail:kdm@essex.ac.uk

    费敏锐  上海大学机电工程与自动化学院教授.主要研究方向为网络化控制系统, 智能机器人系统.E-mail:mrfei@stafi.shu.edu.cn

    通讯作者:

    陈灵 上海大学机电工程与自动化学院助理研究员.主要研究方向为机器人定位与导航.本文通信作者.E-mail:lcheno@shu.edu.cn

Novel Path Curvature Optimization Algorithm for Intelligent Wheelchair to Smoothly Pass a Narrow Space

Funds: 

Key Project of Science and Technology Commission of Shanghai Municipality 14JC1402200

Key Project of Science and Technology Commission of Shanghai Municipality 15411953502

Shanghai Sailing Program 16YF1403700

Shanghai Colleges and Universities Young Teachers Training Funding Scheme ZZSD15088

More Information
    Author Bio:

    Assistant professor in the Department of Computer Science, University of Oxford, Oxford, UK. His research interest covers robot localization and multiple sensor fusion

    Professor at the School of Computer Science and Electronic Engineering, University of Essex, UK, leading robotics research. His research interest covers autonomous robots and networked robots

    Professor at the School of Computer Science and Electronic Engineering, University of Essex, UK. His research interest covers embedded systems and application of robotics

    Professor at the School of Mechatronics Engineering and Automation, Shanghai University. His research interest covers networked control system and intelligent robot system

    Corresponding author: CHEN Ling Assistant professor at the School of Mechatronics Engineering and Automation, Shanghai University. His research interest covers localization and navigation of robots. Corresponding author of this paper
  • 摘要: 为了使用户更感舒适,智能轮椅应该能在具有复杂几何约束的室内环境下平滑地通过狭窄通道.本文提出一个基于路径曲率优化的算法以确保智能轮椅平滑地通过狭窄过道.考虑到路径平滑度取决于路径曲率及其变化率,在通过传感器数据计算出狭窄通道相对于轮椅的位置后,算法以贝塞尔曲线的曲率及其变化率最小为优化目标,以轮椅过通道时的方向及贝塞尔多边形应为凸多边形作为约束,规划出一条平滑的最优路径,然后控制轮椅实时跟踪这条路径.上述过程动态循环运行,实现了智能轮椅平滑通过狭窄通道.仿真中将本文算法同基于A*的路径规划导航算法进行了对比,结果表明本文提出的基于曲率优化的算法可以实现比A*算法路径曲率更小且更加平滑的过狭窄通道过程,并且即使在没有全局地图和定位信息情况下,算法也能控制轮椅平滑地通过狭窄过道.实验中详细阐述了算法的实现过程,实验结果也证实了算法的有效性.
    1)  本文责任编委 程龙
  • 图  1  轮椅在过狭窄门时的情景

    Fig.  1  Scenario of wheelchair in a narrow doorway

    图  2  轮椅过狭窄通道过程原理图

    Fig.  2  The schematic description of the narrow gap passing process

    图  3  轮椅过狭窄通道算法结构图

    Fig.  3  Strategy architecture of narrow gap passing

    图  4  位置误差的计算

    Fig.  4  The calculation of the position error

    图  5  轮椅的参考路径和实际路径

    Fig.  5  Reference path and actual path of wheelchair

    图  6  在场景$A$中每一条路径对应的轮椅的角速度和跟踪误差

    Fig.  6  The angular rate and the tracking errors for each path in Scenario $A$

    图  7  算法比较的仿真环境

    Fig.  7  Simulation environment for algorithm comparison

    图  8  基于本文方法和A*算法的轮椅路径

    Fig.  8  Actual trajectories generated by the proposed method and A* algorithm

    图  9  基于本文方法和A*算法的路径曲率的比较

    Fig.  9  The comparison of trajectory curvature generated by the proposed method and A* algorithm

    图  10  算法每次循环中各模块运行时间及总时间

    Fig.  10  Consuming time of each algorithm module and their total value for each iteration

    图  11  轮椅和激光的配置

    Fig.  11  Configuration of the wheelchair and the lasers

    图  12  实验场景

    Fig.  12  Experimental scenario

    图  13  一次激光扫描数据点及其分段

    Fig.  13  A full scan of laser data points and its expected segmentations

    图  14  两个激光测距仪相对于单个的优势

    Fig.  14  Advantage of two lasers compared with single laser

    图  15  轮椅轨迹

    Fig.  15  Trajectories of the wheelchair

    图  16  轮椅从不同起点到达终点位置时的实际位置

    Fig.  16  Actual wheelchair poses when arriving at the desired pose with different starting poses $A$, $B$, $C$ and $D$

    图  17  轮椅的角速度

    Fig.  17  Angular rate of the wheelchair

    图  18  轮椅从起点$A$和$D$出发过通道轨迹截图((a)~(f)对应于起点$A$, (g)~(l)对应于起点$D$)

    Fig.  18  Snapshots of the wheelchair$'$s trajectories when passing gap, starting from pose $A$ and $D$ ((a)~(f), (g)~(l) are with pose $A$ and $D$, respectively.)

    表  1  每条路径的输入参数

    Table  1  The input parameters for each path

    场景 Ps (m) Pd (m) Pt (m) 1 (Hs, Hd) 2 (Hs, Hd) 3 (Hd, Ht) 4 (Hd, Ht)
    A (0.1, 1.3) (1.6, 1.8) (0.0, 3.5) (-20, 90) (-40, 90) (90, 160) (90, 200)
    B (0.1, 1.3) (1.6, 1.8) (3.4, 3.5) (-10, 90) (10, 90) (90, 20) (90, -20)
    下载: 导出CSV
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  • 收稿日期:  2016-03-01
  • 录用日期:  2016-07-11
  • 刊出日期:  2016-12-01

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