A ROS2 Benchmarking Framework for Hierarchical Control Strategies in Mobile Robots for Mediterranean Greenhouses

TL;DR

This paper introduces a comprehensive benchmarking framework for evaluating hierarchical control strategies of mobile robots in Mediterranean greenhouses, incorporating realistic environmental models, disturbance scenarios, and standardized metrics for fair comparison.

Contribution

It presents a novel, modular benchmarking framework with disturbance modeling and standardized metrics, enabling systematic evaluation of control strategies in realistic agricultural environments.

Findings

Framework effectively differentiates control strategies under varied conditions.

Standardized metrics enable objective comparison of control performance.

Simulation results demonstrate the framework's robustness and extensibility.

Abstract

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.