Publication

Abstract : This article provides a comparative analysis of two bio-inspired optimization algorithms namely Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA)-incorporated along with Model Predictive Control (MPC) scheme for precise trajectory tracking of indoor mobile robots. The performance of the developed GWO-MPC and SSA-MPC controllers is evaluated in an obstacle-rich environment along three distinct trajectories, with comparisons made in terms of tracking accuracy and computational cost. Results indicate that although SSA converges more quickly with 4.80× faster computation, GWO maintains 65.3% lower average tracking error in all cases, especially in sharp-turn or narrow-path situations, achieving 88.7% efficiency in trajectory following. Qualitative analysis combining speed and time metrics reveals SSA achieving 90-95% real-time suitability across all trajectories with computational advantage, while GWO demonstrates 49% average real-time suitability but maintains superior tracking precision, establishing clear trade-offs for deployment scenarios. The results illustrate the potential of bio-inspired optimizers to improve MPC performance for real-time indoor navigation systems.