Adaptive Grid Search Method Using Dynamic Step‐Size Adjustment for Robot‐Tissue Interaction Force Estimation

ABSTRACT Background Reliable force perception is critical in robot‐assisted minimally invasive surgery. However, constraints in end‐effector size and complexity of the surgical environment hamper the integration of force sensors for direct feedback. Methods In this study, an adaptive grid search algorithm with dynamic step‐size adjustment is proposed to optimise the Hunt–Crossley (HC) model parameters for precise force estimation in robotic applications. The method continuously adjusts the step size based on real‐time estimation error, thereby enabling efficient investigation of the parameter space and refined optimisation near the optimal solution. Results Experimental results show that the proposed method improves force estimation accuracy, with a reduction in Maximum Error (ME) of at least 25%, and reductions in Root Mean Square Error (RMSE) and Average Error (AE) of at least 30% compared to conventional fixed‐step approaches. Conclusions These improvements enhance operational safety and also achieve the balance between computational efficiency and perception accuracy.