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    刘宇峰, 姬长英, 田光兆, 等. 自主导航农业机械避障路径规划[J]. 华南农业大学学报, 2020, 41(2): 117-125. doi: 10.7671/j.issn.1001-411X.201909010
    引用本文: 刘宇峰, 姬长英, 田光兆, 等. 自主导航农业机械避障路径规划[J]. 华南农业大学学报, 2020, 41(2): 117-125. doi: 10.7671/j.issn.1001-411X.201909010
    shu
    LIU Yufeng, JI Changying, TIAN Guangzhao, et al. Obstacle avoidance path planning for autonomous navigation agricultural machinery[J]. Journal of South China Agricultural University, 2020, 41(2): 117-125. doi: 10.7671/j.issn.1001-411X.201909010
    Citation: LIU Yufeng, JI Changying, TIAN Guangzhao, et al. Obstacle avoidance path planning for autonomous navigation agricultural machinery[J]. Journal of South China Agricultural University, 2020, 41(2): 117-125. doi: 10.7671/j.issn.1001-411X.201909010
    shu

    自主导航农业机械避障路径规划

    Obstacle avoidance path planning for autonomous navigation agricultural machinery

      摘要
    • 摘要:
      目的  实现自主导航农业机械作业时静态障碍物避障功能。
      方法  在已知工作环境条件下提出2种避障路径规划算法。以农机运动规律为基础，依据障碍物位置和尺寸信息提出单障碍物避障算法。在单障碍物避障算法基础上，依据安全行驶区域大小，参考左右双向避障策略提出双/多障碍物避障算法。
      结果  单障碍物位于不同位置、农机行驶速度为0.3 m·s−1时，行驶路径分别比L算法减少35%、26%，行驶路径累计误差减少53%、82%，方差减少64%、66%；行驶速度为0.5 m·s−1时，行驶路径减少38%、22%，行驶路径累计误差减少66%、62%，方差减少41%、71%。多障碍物路况，农机行驶速度为0.3、0.5 m·s−1时，行驶路径累积误差分别为9.99、4.13 m，方差分别为0.022 1、0.027 0 m2。
      结论  本文算法在行驶路径、行驶路径累计误差、规划路径跟踪稳定性和路况适应性上均体现出一定优势。

       

      Abstract:
      Objective  To realize static obstacle avoidance of autonomous navigation agricultural machinery when it operates in the field.
      Method  Two path planning algorithms of obstacle avoidance were proposed under known working environment. The single obstacle avoidance algorithm was proposed based on the movement rule of agricultural machinery and according to the position and the size of the obstacle. On the basis of the single obstacle avoidance algorithm, according to the size of safe driving area, the double/multiple obstacles avoidance algorithm was proposed according to left and right obstacle avoiding strategies.
      Result  When the single obstacle was located in different locations and the speed of agricultural machinery was 0.3 m·s−1, compared with L algorithm, driving path reduced by 35%, 26%; Accumulative error of driving path reduced by 53%, 82%; Variance reduced by 64%, 66%. When the driving speed was 0.5 m·s−1, driving path reduced by 38%, 22%; Accumulative error reduced by 66%, 26%; Variance reduced by 41%, 71%. When the speed of agricultural machinery was 0.3 and 0.5 m·s−1 under the condition of multiple obstacles, accumulative tracking errors of driving path was 9.99, 4.13 m, and variances were 0.022 1, 0.027 0 m2, respectively.
      Conclusion  The proposed algorithm has some advantages in driving path, accumulative error of driving path, stability of theoretical path tracking and adaptability to road condition.

       

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