Liu Zifu, Sampurno Rizky Mulya, Abeyrathna R M Rasika D, Nakaguchi Victor Massaki, Ahamed Tofael
Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Department of Agricultural and Biosystem Engineering, Universitas Padjadjaran, Sumedang 45363, Indonesia.
Sensors (Basel). 2024 Dec 19;24(24):8113. doi: 10.3390/s24248113.
With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost. Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera's point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. Second, by adopting the URDF format, we built the manipulator's simulation model to be inserted with the reconstructed 3D obstacle map environment. Third, the Bi-RRT algorithm was introduced for path planning, which performs bidirectional expansion simultaneously from the start and targets configurations based on the principles of the RRT algorithm, thereby effectively shortening the time required to reach the target. Subsequently, a validation and comparison experiment were conducted in an artificial orchard. The experimental results validated our method, with the Bi-RRT algorithm achieving reliable collision-free path planning across all experimental sets. On average, it required just 0.806 s and generated 12.9 nodes per path, showing greater efficiency in path generation compared to the Sparse Bayesian Learning (SBL) algorithm, which required 0.870 s and generated 15.1 nodes per path. This method proved to be both effective and fast, providing meaningful guidance for implementing path planning for a 6-DoF manipulator in orchard harvesting tasks.
随着农业劳动力的减少和老龄化，配备高自由度（DoF）操纵器的水果采摘机器人被视为在非结构化和复杂果园环境中执行采摘作业的一种有前途的解决方案。在这样一个复杂的环境中，将末端执行器从其起始位置引导到目标果实同时避开障碍物，对自动采摘中的路径规划构成了重大挑战。然而，现有研究通常依赖于手动构建的环境地图模型，在规划效率和计算成本方面存在局限性。因此，在本研究中，我们引入了一种使用RGB-D相机和双向快速扩展随机树（Bi-RRT）算法的6自由度果园采摘操纵器无碰撞路径规划方法。首先，通过将RGB-D相机的点云数据转换为碰撞几何形状，我们实现了3D障碍物地图重建，使采摘机器人能够检测其工作空间内的障碍物。其次，通过采用统一机器人描述格式（URDF），我们构建了操纵器的仿真模型，并将其插入到重建的3D障碍物地图环境中。第三，引入Bi-RRT算法进行路径规划，该算法基于快速扩展随机树（RRT）算法的原理，从起始配置和目标配置同时进行双向扩展，从而有效缩短到达目标所需时间。随后，在人工果园中进行了验证和比较实验。实验结果验证了我们的方法，Bi-RRT算法在所有实验组中都实现了可靠的无碰撞路径规划。平均而言，它仅需0.806秒，每条路径生成12.9个节点，与稀疏贝叶斯学习（SBL）算法相比，在路径生成方面显示出更高的效率，SBL算法每条路径需要0.870秒，生成15.1个节点。该方法被证明既有效又快速，为在果园采摘任务中实现6自由度操纵器的路径规划提供了有意义的指导。
