Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

• Published in: Journal of field robotics Vol. 41; no. 7; pp. 2427 - 2440
• Main Authors: Yao, Xingbo, Bai, Yuhao, Zhang, Baohua, Xu, Dahua, Cao, Guangzheng, Bian, Yifan
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.10.2024
• Subjects: Adaptive algorithms; agricultural robotics; Autonomous navigation; Greenhouses; Loam; Localization; Mapping; Modules; Multisensor fusion; Navigation systems; Path planning; path planning of mobile robot; Perception; Planning; Pose estimation; Robot dynamics; Robustness; Strategy; Trajectory planning
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22315

The autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.