Research Progress on Key Technology of Apple Harvesting Robots in Structured Orchards

Currently, apple harvesting primarily relies on manual picking, which presents issues such as low efficiency, high costs, and frequent occupational health problems. As the labor shortage intensifies, the timely harvest of fresh apples faces significant challenges, making the use of robots to replace human labor an inevitable trend. With the groundbreaking advancements in standardized orchard production techniques and artificial intelligence technology, apple harvesting automation has achieved new breakthroughs. The key technologies of apple picking robots include apple recognition, apple positioning, robotic arms, motion planning, and end effectors. The performance of these technologies directly determines the efficiency and quality of the harvesting process. This paper provides an overview of the current development status of apple-picking robots. It reviews methods for apple recognition, introduces various sensor-based positioning technologies, studies robotic arms used for apple harvesting, discusses related motion planning algorithms, and analyzes end effectors and their detachment methods. The paper also introduces apple-picking robots that have already been commercialized and identifies the existing issues such as long picking cycles, low success rates, severe fruit damage, and high costs. It points out future directions for commercial development, specifically focusing on reducing the complexity of orchard environments, optimizing recognition and motion planning algorithms, integrating multiple sensors, and enhancing multi-robotic arm cooperation.