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.

中文翻译：

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利用改进的 LeGO‐LOAM 和 OpenPlanner 算法在动态温室环境中进行自主导航和自适应路径规划

温室机器人的自主导航取决于精确的地图、准确的定位信息和强大的路径规划策略。然而，复杂的农业环境给机器人感知和路径规划带来了重大挑战。在这项研究中，提出了一种专为温室农业环境设计的硬件系统，采用多传感器融合来减少复杂环境条件的干扰。此外，还提出了一种基于改进的轻量级和地面优化的激光雷达里程计和测绘（LeGO-LOAM）和 OpenPlanner 的鲁棒自主导航框架。在感知阶段，重定位模块被集成到 LeGO-LOAM 框架中。它包括两个关键步骤——地图匹配和过滤优化，确保更精确的姿态重定位。在路径规划过程中，我们的增强型 OpenPlanner 会考虑地面结构和植物密度。此外，引入滞后策略来增强系统状态转换的稳定性。本文中的导航系统的性能在几种复杂的温室环境中进行了评估。重定位模块的集成显着降低了感知过程中的绝对位姿误差（APE），从而获得更准确的位姿估计和重定位信息。在我们的实验中，APE 至少降低了 24.42%。此外，我们增强的 OpenPlanner 具有规划更安全的轨迹并在实验中实现更稳定的状态转换的能力。结果强调了我们提出的方法的安全性和稳健性，凸显了其在农业机器人自主导航方面的广阔应用前景。