Enhancing performance of global path planning for mobile robot through Alpha–Beta Guided Particle Swarm Optimization (ABGPSO) algorithm

Efficient path planning is essential for mobile robots to navigate from a start to a goal position while avoiding obstacles. Particle Swarm Optimization (PSO) is widely used due to its strong search capabilities, but its standard form suffers from slow convergence and local optima trapping, limiting its performance in complex environments. To address these challenges, this paper proposes an Alpha–Beta Guided Particle Swarm Optimization (ABGPSO) algorithm, incorporating two coefficients, alpha and beta, which utilize a time-varying sigmoid function to dynamically adjust particle movements. This enhancement improves PSO's navigation efficiency, ensuring smoother, collision-free paths while optimizing both travel time and distance. Experiments were carried out in four different layouts related to path-planning environments, and comparisons were made with various existing path-planning algorithms. Through extensive simulations across various static environment maps, we demonstrate that the ABGPSO algorithm outperforms existing state-of-the-art optimization techniques, including Genetic Algorithms (GA), Grey Wolf Optimization (GWO), and modern optimizers like the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and Reptile search algorithm (RSA). The results reveal that our proposed method reduces the mobile robot's travel time by up to 69%, 67%, 72%, and 79% compared to these algorithms, while consistently achieving optimal path lengths. This research contributes to the advancement of mobile robot navigation by providing a novel PSO modification that effectively balances the critical factors of distance, time, and safety in path planning. The results showed that the proposed ABGPSO algorithm reduces the time mobile robots take from start to goal.

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