Distributed environmental sensing and soil moisture gradient navigation in modular robots

Agricultural environments exhibit spatial variations in soil moisture that provide directional information for exploration and monitoring tasks. This work presents a distributed navigation strategy for modular robots based on local soil moisture gradients and neighbor-to-neighbor information exchange. The proposed architecture separates navigation and locomotion into two coordinated layers. The navigation layer estimates movement direction from environmental measurements and obstacle perception. The locomotion layer transforms directional decisions into coordinated motion through coupled oscillators. The proposed method was implemented on an EMeRGe modular robotic platform and was evaluated through 360 physical experiments conducted with three modular configurations under heterogeneous soil moisture conditions. The experimental evaluation compared the proposed method with Hill Climbing, Ant Colony Optimization, and Genetic Algorithm navigation strategies operating under identical sensing and locomotion conditions. The obtained results showed reductions in convergence time, decreases in directional estimation error, increases in navigation efficiency, and lower velocity variance across the evaluated scenarios. These results indicate that local soil moisture gradients can be used to estimate navigation directions without global maps or centralized control. This approach establishes a relationship between environmental sensing, distributed directional estimation, and motion generation in modular robotic systems for agricultural monitoring applications.