# Development of a Combined Orchard Harvesting Robot Navigation System
## 1 Introduction
### Related Works  
As early as 2004, [PERSON] et al. [13] proposed the concept of master-slave navigation based on the combined harvesting method for hay in large fields. The master-slave navigation was divided into two modes: command navigation and follow navigation [13; 14]. A review of the literature reveals that the earliest record of master-slave navigation robots in orchards was the orchard spraying robots proposed by [PERSON] et al. [15] in 2012, which used a centralized controller as the master and operated spraying robots with commanded navigation in the planned operation area. Similarly, [PERSON] et al. [16], and [PERSON] et al. [17] first used a UAV to acquire ground information and then used command navigation based on ground images to operate the ground robot along the planned operation path. [PERSON] et al. [18] used two UAVs and a ground robot to monitor the environment in an orchard, with the ground robot traveling ahead as the master and the UAV following as the slave. The experimental results showed that the master-slave robot could maintain a stable formation under this navigation mode. These studies all used one navigation mode, either command navigation or follow navigation. In a single master-slave navigation mode,  
Figure 1: Manual apple picking and handling. (**a**) Climbing ladders for apple picking; (**b**) picking and placing in fruit boxes; (**c**) placing apple boxes on the transport truck. the robots can only achieve continuous path tracking or maintain a fixed formation, which is not very flexible. In terms of communication technology, there is currently little research on the communication of multi-mobile robots in orchards. [PERSON] et al. [19] proposed an ad hoc on-demand distance vector routing (AODV) protocol based on a prediction model of the reception strength of Wi-Fi signals in an orchard and built a set of small physical platforms for communication based on a leader-follow structure to verify the practicality of the routing protocol in a dynamic environment. However, this needs to be tested in a real orchard scenario. [PERSON] [20] installed XBEE modules on multiple picking bags and sent the data to a centralized controller via a wireless network. However, picking bags with XBEE modules are usually fixed in place, and XBEE requires high data throughput and is unsuitable for large data volume communication. In terms of master-slave robot cooperative navigation, the problem of autonomous navigation of the master robot needs to be addressed first. The current research on autonomous navigation of orchard robots mainly focuses on automatic travel between rows of fruit trees, especially the use of light detection and ranging (LIDAR) to extract tree rows or detect trees and labels, which is regarded as a reliable method of autonomous navigation. For example, [PERSON] et al. [21] set landmarks at the ends of rows of fruit trees and used a laser scanner to detect the landmark information and build a local map of the orchard to ensure the robot's autonomy in the orchard. [PERSON], et al. [22] fused a 2-D laser scanner, wheel, and steering encoder sensor data, and used the laser scanner to detect the landmarks at both ends of the orchard rows to bring about the vehicle's straight and cross-row turns in the orchard. [PERSON], et al. [23] fused vision and LIDAR information for detecting and distinguishing non-trunked trees with 96.64% detection accuracy. [PERSON], et al. [24] used a 3-D laser scanner to detect the features of posts and tree trunks in a pergola structure orchard to determine the robot's travel centerline. [PERSON] and [PERSON] [25] relied on a 2-D laser scanner to detect landmarks for real-time robot map building and localization in an apple orchard. [PERSON] et al. [26] used a probabilistic particle filter (PF) algorithm combined with LIDAR detection to achieve autonomous robot navigation within fruit tree rows by detecting tree rows. [PERSON], et al.