Controller Tuning and Robustness for Autonomous Ground Robots in Eucalyptus Plantations

Ground robots are proposed for inventory in commercial eucalypt plantations, where inter-row corridors leave a platform about one metre of lateral room for error. This study asked, entirely in simulation with parameters plausible rather than calibrated to a platform or stand, when controller tuning changes the outcome. Three tuning algorithms (finite-difference descent, Bayesian optimisation and covariance matrix adaptation evolution strategy) and two robust controllers (sliding-mode and active disturbance rejection) were compared across two commercial spacings and a factorial ablation of state estimation and terrain disturbance. In 1440 simulated missions, the success rate was 0.999. Intermittent satellite positioning consumed 18% of the lateral error budget and underlay every stem contact, whereas terrain disturbance cost 18% of traversal time and 2% of the budget; a filter matched to the plant left this unchanged. Tuning the algorithm and control law moved clearance by at most 2% of the budget. Over 64,512 missions under amplified disturbance, breakdown was predicted by integral action rather than the stability margin, and an anti-windup clamp showed this effect to be mostly integrator windup. Within this model, safety is bounded by localisation rather than the control loop; the magnitudes depend on the simulated parameters and are hypotheses for field testing.