# Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance
## 1 Introduction
The QZSS is a four-satellite regional time transfer system and a satellite-based augmentation system development by the Japanese government to enhance the United States-operated Global Positioning System in the Asia-Oceania regions, with a focus on Japan. QZSS is a Japanese satellite positioning system composed mainly of satellites in quasi-zenith orbits (QZO). QZSS (Michibiki) has been operated as a four-satellite constellation from November 2018, and three satellites are visible at all times from locations in the Asia-Oceania region. QZSS can be used in an integrated way with GPS, ensuring a sufficient number of satellites for stable, high-precision positioning. Although the price of these sensors is currently high, the cost is expected to decrease in the future. To keep the current cost of the system down, the developed system mainly relies on Lidar sensors. However, integration of GPS is feasible for better results. To navigate accurately, a robot needs a map of the environment, which marks the obstacles and free areas. It also needs to localize itself on the map. Then, given the start and goal locations, the robot can navigate through the free areas avoiding the obstacles using path planning algorithms. To realize this, mobile robots are equipped with sensors to perceive the external world. These include sensors such as Lidar, cameras, RGBD (RGB color and Depth) sensors, IMU, GPS, and external sensors [4]. In addition, robots are also equipped with a SLAM (Simultaneous Localization and Mapping) [5; 6] module, which uses data from these sensors to build a map of the environment and localize its position in the map. The SLAM module extracts features from the sensor data, which are used as landmarks to accurately build the map and localize itself in it. Failure to robustly extract landmarks results in a wrong map and localization errors, which eventually affects tasks performed by the mobile robot. Robotics for vineyards have been well studied for different applications in both academics and industries [7]. Both single [8; 9] and multi-robot scenarios [10] for localization with trunk recognition have been proposed. Other researchers have focused on autonomous pruning [11] and total yield estimation [12; 13]. Similarly, managing the irrigation in vineyards has been studied in [14]. In all these works, autonomous safe navigation is assumed to be available with the robot and algorithms have been proposed [15]. In many works, robotics approaches have been coupled with IoT-based approaches for vineyards [16; 17; 18; 19; 20; 21; 22; 23; 24]. It should be noted that all of these works implicitly depend on the navigation module of a mobile robot. Particularly, tasks such as vineyard monitoring [25; 26; 23] cannot be successfully executed without safe autonomous navigation. Dynamic obstacles must also be considered for navigation. A wireless sensor network for vineyard monitoring that uses image processing is proposed in [25]. Figure 2 shows the standing of the proposed research (yellow block) in vineyard field robots. As mentioned above, researchers have focused on automation of several tasks in vineyards such as grape harvesting, weed removal, monitoring, and yield-estimation. From a system development point of view, it can be suggested that these are high level applications, which assume the availability of certain core modules. The core modules include 2D and 3D mapping, robot's localization, robot control, and path planning with obstacle avoidance. The core modules support the high level applications. For example, the robotic grape harvesting application requires a 3D map, which marks the grapes and stem that comes from a mapping module. Similarly, the robot needs to localize itself in the vineyard to perform harvesting at appropriate areas. This requires a localization module. Moreover, a path planning module is required to move the robot while avoiding  
obstacles to appropriate areas of the vineyard that need harvesting.