Li Jun, Li Haomin, Zeng Ye, Jiang Runpeng, Mai Chaodong, Ma Zhe, Cai Jiamin, Xiao Boyi
College of Engineering, South China Agricultural University, Guangzhou, China.
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.
Front Plant Sci. 2024 Nov 1;15:1403385. doi: 10.3389/fpls.2024.1403385. eCollection 2024.
Path planning algorithms are challenging to implement with mobile robots in orchards due to kinematic constraints and unstructured environments with narrow and irregularly distributed obstacles.
To address these challenges and ensure operational safety, a local path planning method for orchard mowers is proposed in this study. This method accounts for the structural characteristics of the mowing operation route and utilizes a path-velocity decoupling method for local planning based on following the global reference operation route, which includes two innovations. First, a depth-first search method is used to quickly construct safe corridors and determine the detour direction, providing a convex space for the optimization algorithm. Second, we introduce piecewise jerk and curvature restriction into quadratic programming to ensure high-order continuity and curvature feasibility of the path, which reduce the difficulty of tracking control. We present a simulation and real-world evaluation of the proposed method.
The results of this approach implemented in an orchard environment show that in the detouring static obstacle scenario, compared with those of the dynamic lattice method and the improved hybrid A* algorithm, the average curvature of the trajectory of the proposed method is reduced by 2.45 and 3.11 , respectively; the square of the jerk is reduced by 124 and 436 / , respectively; and the average lateral errors are reduced by 0.55 and 4.97 , respectively, which significantly improves the path smoothness and facilitates tracking control. To avoid dynamic obstacles while traversing the operation route, the acceleration is varied in the range of -0.21 to 0.09 / . In the orchard environment, using a search range of 40 × 5 m and a resolution of 0.1 , the proposed method has an average computation time of 9.6 . This is a significant improvement over the open space planning algorithm and reduces the average time by 12.4 compared to that of the dynamic lattice method, which is the same as that of the structured environment planning algorithm.
The results show that the proposed method achieves a 129% improvement in algorithmic efficiency when applied to solve the path planning problem of mower operations in an orchard environment and confirm the clear advantages of the proposed method.
由于运动学约束以及果园中存在狭窄且分布不规则的障碍物的非结构化环境，路径规划算法在果园移动机器人上的实现具有挑战性。
为应对这些挑战并确保操作安全，本研究提出了一种果园割草机的局部路径规划方法。该方法考虑了割草作业路线的结构特征，并基于跟随全局参考作业路线采用路径 - 速度解耦方法进行局部规划，其中包括两项创新。首先，使用深度优先搜索方法快速构建安全走廊并确定绕行方向，为优化算法提供凸空间。其次，将分段加加速度和曲率约束引入二次规划，以确保路径的高阶连续性和曲率可行性，从而降低跟踪控制的难度。我们对所提出的方法进行了仿真和实际评估。
在果园环境中实施该方法的结果表明，在绕行静态障碍物场景下，与动态格点法和改进的混合A*算法相比，所提方法轨迹的平均曲率分别降低了2.45和3.11；加加速度的平方分别降低了124和436 / ；平均横向误差分别降低了0.55和4.97，这显著提高了路径平滑度并便于跟踪控制。在穿越作业路线时为避免动态障碍物，加速度在 -0.21至0.09 / 的范围内变化。在果园环境中，使用40 × 5 m的搜索范围和0.1 的分辨率，所提方法的平均计算时间为9.6 。这相对于开放空间规划算法有显著改进，与动态格点法相比平均时间减少了12.4 ，与结构化环境规划算法相同。
结果表明，所提方法在应用于解决果园环境中割草机作业的路径规划问题时，算法效率提高了129%，证实了所提方法的明显优势。
