果园环境下移动采摘机器人导航路径优化

针对移动采摘机器人在果园作业时,果树较大冠层与行人等障碍物易影响机器人行驶的突出问题,该研究提出了一种基于改进人工势场法的机器人行间导航路径优化方法。首先,通过移动采摘机器人搭载的固态激光雷达实现果园行间三维点云信息获取,运用地面平面算法去除果园地面点云,提取了果园垄行与果树冠层点云。其次,采用最小二乘法(Least Squares Method, LSM)、霍夫(Hough)变换和随机采样一致性(Random Sample Consensus, RANSAC)3种方法对果园垄行点云数据进行了垄行线和初始路径的提取。最后,通过舍弃引力势场,建立了果树冠层轮廓点云势场,优化初始路径以躲避较大的果树冠层与行人障碍物。从实时性与抗噪能力两个方面,分别对利用LSM、Hough变换和RANSAC方法所提取的初始路径结果进行了分析,结果表明3种方法均可成功提取垄行线与初始路径,其中RANSAC实时性最优,平均运行时间约为0.147×10-3 s,标准差为0.014×10-3 s,且具有较好的抗噪能力。在RANSAC提取初始路径的基础上使用改进人工势场法对初始路径进行优化,避免了传统人工势场法易陷入震荡的问题。经改进人工势场法优化后的路径将障碍物点云距导航路径的最短距离由0.156 m提高至0.863 m,且平均耗时0.059 s,标准差为0.007 s,表明该优化方法具备实时优化路径以避开障碍物的能力。该研究提出的基于改进人工势场法的机器人行间导航路径优化方法基本满足安全性与实时性要求,为移动采摘机器人在果园环境下自主导航提供了技术参考。

Optimization of the navigation path for a mobile harvesting robot in orchard environment

Obstacles such as large irregular fruit tree canopy and pedestrians are hidden dangers that hinder the movement of mobile picking robots in orchard environments. To improve the safety of mobile picking robot in the orchard environment and preventing the collision between mobile picking robot and obstacles, this study took the spindle-shaped apple orchard as the research object and proposed a mobile robot navigation path optimization method based on the improved artificial potential field. The mobile picking robot was composed of robot chassis, LiDAR (RS-LiDAR-M1, Suteng Innovation Technology Co., Ltd. China), the main controller (Jetson TX2, NVIDIA Co. Ltd., USA), and the picking arm, the main controller installed the ubuntu16.04 LTS operating system, and developed the program based on Robot Operating System (ROS) and Point Cloud Library (PCL). The main steps of the mobile robot navigation path optimization method based on the improved artificial potential field included point cloud preprocessing, extraction of ridgelines, and optimization of initial path. Firstly, the LiDAR carried by the mobile picking robot collected the three-dimensional point cloud between the rows of the orchard. The three-dimensional point cloud was processed through filtering, down-sampling filtering, and statistical filtering, and the ground plane algorithm removed the orchard ground point cloud and extract the orchard ridge and fruit tree canopy point cloud. Secondly, the Least Square Method (LSM), Hough Transform, and Random Sampling Consensus (RANSAC) extracted the ridgelines from the orchard ridge point cloud. The middle lines of the two sides of the ridgelines were used as the initial path. Finally, the artificial potential field was improved by discarding the gravitational potential field and established the potential field of the fruit tree canopy profile point cloud. The initial path was discretized into discrete points, and each discrete point was optimized in turn according to the improved artificial potential field, and then the optimized discrete points were fitted by quadratic B-spline curve to obtain the optimized path which had the ability to avoid large fruit tree canopy and pedestrian obstacles. The results of the extracted initial path by LSM, Hough transform, and RANSAC were analyzed in terms of real-time performance and anti-noise ability. The results showed that the three methods successfully extracted ridgeline and initial path, and RANSAC had the best real-time performance and the average processing time was about 0.147 × 10-3 s, the standard deviation was 0.014×10-3 s, and it had good anti-noise ability. Based on the extracted initial path by RANSAC, the improved artificial potential field method was used to optimize the initial path, which avoided the problem that the traditional artificial potential field method was easy to fall into oscillation. The shortest distance between the obstacle point cloud and the navigation path was increased from 0.156 m to 0.863 m, and the average optimization time and the standard deviation was 0.059 and 0.007 s, respectively, which indicated the optimization method basically had the ability to optimize the path in real-time to avoid obstacles. The method of optimizing the navigation path proposed in this study basically could meet the requirements of safety and real-time, which could provide a technical reference for the autonomous navigation of mobile picking robots in the orchard environment.