Search

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. [ABSTRACT FROM AUTHOR]

Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported. [ABSTRACT FROM AUTHOR]