# Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance
## 2 Pillar Detection from Lidar Sensor Data
The algorithm estimated that the person is approaching from the right side of the lane from the point of view of the robot. Hence, a left turn was decided. A smooth trajectory was generated using the

\[c_{y}=m_{P_{1}O}\cdot c_{x}-m_{P_{1}O}\cdot p_{x1}+p_{y1}. \tag{6}\]4 (**a**–**h**) shows the successive snapshots of autonomous robot navigation in the center of vineyard’s lane using the proposed algorithm. The video of the experiment is provided in the supplementary section. \[c_{y}=m_{P_{1}O}\cdot c_{x}-m_{P_{1}O}\cdot p_{x1}+p_{y1}. \tag{6}\]5 Robotnik’s Summit-XL robot used in the experiment with UTM-30 LX Lidar, camera, and control computer. proposed algorithm and the robot started executing it, as shown in Figure 12g-j. Once the robot has crossed the person (Figure 12k), the robot then generates a trajectory to resume the center of the lane. This is shown in Figure 12l-o. The robot then resumes navigation in the center of the lane, as shown in Figure 12p-t. The total execution time of pillar detection and smooth trajectory detection was within 70 milliseconds. \[c_{y}=m_{P_{1}O}\cdot c_{x}-m_{P_{1}O}\cdot p_{x1}+p_{y1}. \tag{6}\]6 (**a–t**) Timely snapshots of dynamic obstacle (incoming person) avoidance in the vineyard using the proposed algorithm. The video of the experiment is provided in the supplementary section. It should be noted that there could be scenarios when there are obstacles on both sides of the lane, or lane traversal is not possible. Although this case was not tested in the experiments, the algorithm has a feature that, for such cases, the robot stops its navigation for safety and waits until the obstacles have cleared. ## 5 Conclusions

In this paper, we proposed an algorithm for autonomous and safe navigation of mobile robots in vineyards. A robust obstacle detection is inherent to mobile robots in vineyards for different applications such as harvesting and monitoring. The algorithm detects the pillars fixed in the vineyards from Lidar data using a symmetric derivative. Compared to image processing-based detection algorithms, the proposed algorithm is not prone to changing the brightness of the environment and can also be used in low light. The robot can navigate the center of the lane from the extracted pillar positions. An algorithm for smooth collision avoidance for static and dynamic obstacles was also proposed. The parameters of the algorithm can be adjusted to keep the robot safe from both the grapes and the obstacles. The proposed algorithm was tested in both simulation and actual environments. In the future, we plan to test the algorithm in more complex scenarios with different applications such as weed detection and harvesting. Using different sensors such as a camera and Lidar sensor for improved pillar and obstacle detection in the presence of tall weeds is also considered as future work. Video of the proposed monitoring system: [[https://www.dropbox.com/s/jw6](https://www.dropbox.com/s/jw6) vr85 fb404 ll5/safe_vineyard_navigation_ravankar.mp4?dl=0]([https://www.dropbox.com/s/jw6](https://www.dropbox.com/s/jw6) vr85 fb404 ll5/safe_vineyard_navigation_ravankar.mp4?dl=0). [PERSON]. ([PERSON]) conceived the idea; [PERSON]. ([PERSON]) and [PERSON] designed and performed the experiments; [PERSON]. ([PERSON]) helped with proof checking and visualizations; [PERSON] made valuable suggestions to analyze the data and improve the work; [PERSON]. ([PERSON]) wrote the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by JSPS KAKENHI Grant Numbers JP21K14117, and JP21K14115. Not applicable. The authors declare no conflict of interest. ## References

* (1)
* (2) Japan's Ministry of Agriculture, Forestry and Fisheries (MAFF). Census of Agriculture in Japan. 2015.