Research on Navigation Path Extraction and Obstacle Avoidance Strategy for Pusher Robot in Dairy Farm

Fuyang Tian, Xinwei Wang, Sufang Yu, Ruixue Wang, Zhanhua Song, Yinfa Yan, Fade Li, Zhonghua Wang and Zhenwei Yu
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Fuyang Tian: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Xinwei Wang: Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai’an 271018, China
Sufang Yu: College of Life Sciences, Shandong Agricultural University, Tai’an 271018, China
Ruixue Wang: Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Zhanhua Song: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Yinfa Yan: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Fade Li: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Zhonghua Wang: College of Animal Science and Technology, Shandong Agricultural University, Tai’an 271018, China
Zhenwei Yu: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China

Agriculture, 2022, vol. 12, issue 7, 1-23

Abstract: Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems of labor-intensive, labor-intensive, and inability to push material in time at night, etc., in this study, an autonomous navigation pusher robot based on 3D lidar is designed, and an obstacle avoidance strategy based on the improved artificial potential field method is proposed. Firstly, the 3D point cloud data of the barn is collected by the self-designed pushing robot, the point cloud data of the area of interest is extracted using a direct-pass filtering algorithm, and the 3D point cloud of the barn is segmented using a height threshold. Secondly, the Least-Squares Method (LSM) and Random Sample Consensus (RANSAC) were used to extract fence lines, and then the boundary contour features were extracted by projection onto the ground. Finally, a target influence factor is added to the repulsive potential field function to determine the principle of optimal selection of the parameters of the improved artificial potential field method and the repulsive direction, and to clarify the optimal obstacle avoidance strategy for the pusher robot. It can verify the obstacle avoidance effect of the improved algorithm. The experimental results showed that under three different environments: no noise, Gaussian noise, and artificial noise, the fence lines were extracted using RANSAC. Taking the change in the slope as an indicator, the obtained results were about −0.058, 0.058, and −0.061, respectively. The slope obtained by the RANSAC method has less variation compared to the no-noise group. Compared with LSM, the extraction results did not change significantly, indicating that RANSAC has a certain resistance to various noises, but RANSAC performs better in extraction effect and real-time performance. The simulation and actual test results show that the improved artificial potential field method can select reasonable parameters and repulsive force directions. The optimized path increases the shortest distance of the obstacle point cloud from the navigation path from 0.18 to 0.41 m, where the average time is 0.059 s, and the standard deviation is 0.007 s. This shows that the optimization method can optimize the path in real time to avoid obstacles, basically meet the requirements of security and real-time performance, and effectively avoid the local minimum problem. This research will provide corresponding technical references for pusher robots to overcome the problems existing in the process of autonomous navigation and pushing operation in complex open scenarios.

Keywords: dairy farm; pusher robot; path extraction; obstacle avoidance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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