Mobile robots operating in agroindustrial environments, such as Mediterranean greenhouses, are subject to challenging conditions, including uneven terrain, variable friction, payload changes, and terrain slopes, all of which significantly affect control performance and stability. Despite the increasing adoption of robotic platforms in agriculture, the lack of standardized, reproducible benchmarks impedes fair comparisons and systematic evaluations of control strategies under realistic operating conditions. This paper presents a comprehensive benchmarking framework for evaluating mobile robot controllers in greenhouse environments. The proposed framework integrates an accurate three dimensional model of the environment, a physics based simulator, and a hierarchical control architecture comprising low, mid, and high level control layers. Three benchmark categories are defined to enable modular assessment, ranging from actuator level control to full autonomous navigation. Additionally, three disturbance scenarios payload variation, terrain type, and slope are explicitly modeled to replicate real world agricultural conditions. To ensure objective and reproducible evaluation, standardized performance metrics are introduced, including the Squared Absolute Error (SAE), the Squared Control Input (SCI), and composite performance indices. Statistical analysis based on repeated trials is employed to mitigate the influence of sensor noise and environmental variability. The framework is further enhanced by a plugin based architecture that facilitates seamless integration of user defined controllers and planners. The proposed benchmark provides a robust and extensible tool for the quantitative comparison of classical, predictive, and planning based control strategies in realistic conditions, bridging the gap between simulation based analysis and real world agroindustrial applications.

翻译：在农业工业环境中运行的移动机器人，例如地中海温室，面临着具有挑战性的条件，包括不平坦的地形、变化的摩擦力、有效载荷变化以及地形坡度，所有这些都显著影响控制性能和稳定性。尽管机器人平台在农业中的应用日益增多，但缺乏标准化、可复现的基准测试阻碍了在真实操作条件下对控制策略进行公平比较和系统评估。本文提出了一个用于评估温室环境中移动机器人控制器的综合性基准测试框架。该框架集成了精确的环境三维模型、基于物理的模拟器以及一个包含低层、中层和高层控制层的分层控制架构。定义了三个基准测试类别，以实现从执行器级控制到完全自主导航的模块化评估。此外，明确建模了三种扰动场景——有效载荷变化、地形类型和坡度——以复现真实的农业条件。为确保客观且可复现的评估，引入了标准化的性能指标，包括平方绝对误差（SAE）、平方控制输入（SCI）以及复合性能指数。采用基于重复试验的统计分析来减轻传感器噪声和环境变异性的影响。该框架通过一个基于插件的架构进一步增强，该架构便于用户自定义控制器和规划器的无缝集成。所提出的基准测试为在真实条件下定量比较经典控制、预测控制和基于规划的控制策略提供了一个稳健且可扩展的工具，弥合了基于仿真的分析与真实农业工业应用之间的差距。