杨洋, 温兴, 马强龙, 张刚, 程尚坤, 齐健, 陈志桢, 陈黎卿. 基于贝塞尔曲线的动态识别区农机避障路径实时规划[J]. 农业工程学报, 2022, 38(6): 34-43. DOI: 10.11975/j.issn.1002-6819.2022.06.004
    引用本文: 杨洋, 温兴, 马强龙, 张刚, 程尚坤, 齐健, 陈志桢, 陈黎卿. 基于贝塞尔曲线的动态识别区农机避障路径实时规划[J]. 农业工程学报, 2022, 38(6): 34-43. DOI: 10.11975/j.issn.1002-6819.2022.06.004
    Yang Yang, Wen Xing, Ma Qianglong, Zhang Gang, Cheng Shangkun, Qi Jian, Chen Zhizhen, Chen Liqing. Real time planning of the obstacle avoidance path of agricultural machinery in dynamic recognition areas based on Bezier curve[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(6): 34-43. DOI: 10.11975/j.issn.1002-6819.2022.06.004
    Citation: Yang Yang, Wen Xing, Ma Qianglong, Zhang Gang, Cheng Shangkun, Qi Jian, Chen Zhizhen, Chen Liqing. Real time planning of the obstacle avoidance path of agricultural machinery in dynamic recognition areas based on Bezier curve[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(6): 34-43. DOI: 10.11975/j.issn.1002-6819.2022.06.004

    基于贝塞尔曲线的动态识别区农机避障路径实时规划

    Real time planning of the obstacle avoidance path of agricultural machinery in dynamic recognition areas based on Bezier curve

    • 摘要: 为解决农机田间作业避障路径实时规划问题,该研究提出一种在动态识别区内利用三阶贝塞尔曲线实时规划农机避障路径算法。首先构建农机作业行走动态识别区,在动态识别区内利用激光雷达感知障碍物。然后利用障碍物信息计算避障路径控制点选取范围,生成满足农机最小转弯半径等多约束条件下的路径簇,同时以曲率最小为目标从路径簇中选取最优避障路径。最后进行避障路径实时规划试验。试验结果表明,本文算法规划的避障路径最大曲率和平均曲率分别为0.126和0.054 m-1;路径跟踪过程中产生的最大横向误差和平均横向误差分别为0.12和0.057 m;拖拉机到障碍物外轮廓的距离大于0.375 m。和现有算法比较,本文算法规划的避障路径最大曲率和平均曲率分别减少25.9%和42.6%,路径跟踪过程中产生的最大横向误差和平均横向误差分别减少36.8%和28.8%。研究结果可为拖拉机无人驾驶作业提供技术支撑。

       

      Abstract: Abstract: This study aims to realize the real-time planning of obstacle avoidance paths for the agricultural machinery in the field operation using Bezier curve in the dynamic recognition area. The dynamic identification area of the agricultural machinery unit was firstly constructed for the operation walking, according to the tractor length and operation width, where the radar coordinate system (X-O'-Y) was established using the installation position of LiDAR. The obstacles were then identified in the dynamic identification area. The specific steps were as follows: 1) LiDAR was used to collect the point cloud data of the obstacle in the dynamic recognition area; 2) The point cloud information was processed into the three-dimension; 3) The obstacle was used to fit the obstacle envelope cylinder, then to project into the X-O'-Y coordinate system for the envelope circle (the center and radius). Then, the conversion model was established for the coordinate system, according to the installation position of Beidou Positioning Equipment and LiDAR, in order to realize the mutual conversion of longitude and latitude coordinates under the world standard longitude and latitude coordinate system (lon-o-lat coordinate system) and rectangular coordinates under X-O'-Y coordinates. According to the position of the obstacle in the X-O'-Y coordinate system, the obstacle avoidance path was divided into the execution and regression paths. When the ordinate of the circle center of the obstacle envelope contour circle was less than 1/3 of the length of the dynamic recognition area, the obstacle avoidance regression path was planned, otherwise, the obstacle avoidance execution path was planned. Finally, the obstacle avoidance path was planned as follows. 1) The coordinate system conversion model and Beidou positioning equipment were used to convert the collected current position of agricultural machinery from the longitude and latitude coordinates in lon-o-lat coordinate system to rectangular coordinates in X-O'-Y coordinate system; 2) The selection range of obstacle avoidance path control points using the third-order Bessel curve was calculated using the current position, heading angle, center coordinates and radius of obstacle envelope circle of agricultural machinery. The path cluster satisfied the minimum turning radius. 3) Taking the minimum curvature as the goal, the optimal obstacle avoidance path was selected from the path cluster for the coordinate transformation during tractor operation. The dynamic recognition area was reconstructed to repeat the above steps for the obstacle avoidance path. A real-time planning experiment on the obstacle avoidance path was carried out to verify the planning. The experimental results show that the maximum and average curvature of the obstacle avoidance path were 0.126 and 0.054 m-1, respectively, which were reduced by 25.9% and 42.6% than before, respectively. The maximum and average lateral error in the process of path tracking were 0.12 and 0.057 m, respectively, which were reduced by 36.8% and 28.8% than before, respectively. The distance from the tractor to the outer contour of the obstacle was greater than 0.375 m. Therefore, the obstacle avoidance path fully met the minimum turning radius, where the agricultural machinery can safely, effectively, and quickly avoid the static obstacles. The finding can provide technical support for the tractor unmanned operation.

       

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