# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 1 Introduction  
With the continuous increase in the global population, food production must also be increased. However, modern society is facing challenges in food production due to the decline in the agricultural workforce caused by industrialization. To address these issues, agricultural automation technology is advancing and innovations in the field of harvesting robots are expected to contribute to improving global food productivity [1, 2, 3]. Fruit harvesting with robots requires the precise identification of tree and target fruit locations. However, the irregular growth patterns of trees and fruits make them difficult to detect. Furthermore, it is crucial to avoid damaging branches or other harvestable items during the harvesting process. Fruit harvesting using robotic systems requires a computer vision system to detect target objects and the surrounding environment, along with a harvesting method for the manipulator that avoids causing damage to other parts of the tree [4]. To address these challenges, significant research has been conducted. Related works are roughly divided into two main areas: computer vision systems for detecting target fruits and obstacles, and collision-free path planning for the robotic manipulator to avoid damaging fruits and trees. In robotic fruit harvesting, RGB-D cameras or Lidar sensors are mainly used. RGB-D cameras are commonly used for fruit recognition based on color information or for measuring the position of fruits [5, 6, 7, 8, 9]. Although RGB-D cameras are sensitive to surrounding lighting conditions, their advantages in cost and computational efficiency make themsuitable for practical applications in agriculture [10]. For overall mapping of orchards or precise fruit shape measurements, Lidar is used [11; 12; 13; 14; 15; 16; 17]. High costs made it difficult to use in agriculture in the past, but advancements have made it more affordable and accessible. To overcome individual limitations and enhance overall accuracy, research that integrates multiple vision systems is underway. Trees grow randomly in nature, and fruits typically cluster together. Because of these characteristics, a harvesting robot may collide with tree branches and nearby fruits. Therefore, it is crucial to consider the surrounding environment when generating robot paths to minimize damage to the tree, its branches, and nearby fruits during harvesting. Commonly used path planning algorithms for robot arm include the A*, rapidly exploring random tree (RRT) and artificial potential field (APF) algorithms. The A* algorithm has the advantage of reaching the destination while avoiding obstacles and finding the shortest path, but it has a drawback in that generating paths in three-dimensional space becomes computationally expensive due to an excessive number of grid points [18]. RRT generates paths by growing a tree through random sampling, leading to non-smooth paths and detours in three-dimensional spaces. The branches that are extended from detours can lead to inefficiency in path planning or computation time. The efficiency of path generation directly impacts the operational efficiency of harvesting robots. Thus, it requires modification to minimize computation time. An algorithm called AtBi-RRT was proposed in [19], based on the Bi-RRT algorithm [20], to accelerate path decision speed by simultaneously growing trees from the initial and the target positions, along with the application of a target gravity concept and an adaptive parameter adjustment method. The proposed algorithm was used in collision-free motion for a litchi-picking robot, with improved planning time. Another algorithm called tGSRT, incorporating the concept of target gravity for increased speed and a GA for path optimization, was proposed in [21] for litchi-picking robot path planning. APF offers the advantages of simplicity and high speed computation, but it easily stuck in local minima and has oscillations in the generated paths.