# Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance
## 1 Introduction
Similarly, a path planning module with obstacle avoidance is indispensable for weed removal, monitoring, and yield estimation applications. Our previous work [28] focused on the development of a monitoring system for vineyards using mobile robots. Vineyards have large areas and manual monitoring consumes a lot of time and effort. Hence, autonomous mobile robots equipped with side facing monocular cameras were used to record the images and transmit them to the farmers who can monitor the vineyard on their mobile or tablet. The recorded images are labeled by area and pillar numbers for the farmer to monitor specific areas of the vineyard. This inherently depends on a reliable navigation module. For yield estimation applications, appropriate sensors such as multispectral and hyperspectral cameras can be fixed on the robot. Similarly, a manipulator and gripper mechanism can be mounted on the robot for harvesting. Weed removal may require a special cutting mechanism. However, the core module for the mobile base on which these sensors will be mounted will remain unchanged. This work is an extension of our previous work [28], which focused on developing a monitoring system for vineyards using autonomous mobile robots. Our previous work relied on detecting pillars from cameras through image processing. However, it had the drawbacks that pillar detection relied on good light conditions, and only the pillars adjacent to the robot were detected with no depth information. Moreover, the focus was on numbering the pillars and generating semantic data for monitoring purposes. However, this paper focuses on the navigational aspects of the autonomous mobile robots in vineyards. Moreover, it uses Lidar data to extract the position of pillars in the vineyard for navigation. In addition, Lidar sensor data are also used to estimate the position of obstacles in the vicinity. It should be noted that cameras can also be used to detect the pillars and the obstacles. Monocular cameras have also been used for obstacle detection in many works [29; 30]. The accuracy of monocular camera-based depth estimation is typically lower than direct estimation from Lidar or stereo cameras for real-time scenarios. Recently, depth estimation from monocular cameras using deep learning techniques has been proposed in [31; 32; 33]. However, the accuracy of depth estimation in such methods is lower than direct estimation using Lidar. Monocular cameras have the advantage of full-body pose and orientation recognition, which can be useful for obstacle avoidance. Hence, a combination of cameras and Lidar sensors is expected to produce better results and is considered for future works. At the current stage, the proposed work uses a straightforward method of pillar and obstacle detection using a Lidar sensor. The rest of the paper is organized as follows. Section 2 explains the algorithm for pillar detection from a Lidar sensor. Section 3 explains the algorithm for trajectory smoothing while avoiding the obstacles. Section 4 highlights the results of experiments in an actual vineyard. Finally, Section 5 concludes this paper. Figure 2: Standing of proposed research (yellow block) in Vineyard robots. Path planning and obstacle avoidance is a core module of mobile robots that supports top-level applications such as harvesting, weed removal, monitoring, and estimation.