Bezier curve-based mobile robot path planning using the Proposed Enhanced Firefly Optimization algorithm

Abstract

In the field of mobile robot navigation, the pursuit of optimal path planning and effective obstacle avoidance has evolved to unprecedented levels. This study introduces a novel hybrid methodology for mobile robot navigation, combining an enhanced Firefly Algorithm (FA) with Bezier curve-based path smoothing to achieve optimal trajectory planning and obstacle avoidance. The refined FA improves exploration dynamics by optimizing the fixed step size and attractiveness parameter, ensuring faster convergence and reduced computational overhead. Simultaneously, the Bezier curve technique facilitates smooth transitions, minimizing abrupt directional shifts and enhancing path continuity. The approach aims to determine the shortest and most efficient path between initial and target points, with control points in the Bezier curve playing a pivotal role in shaping trajectory fluidity and overall path length. Comparative evaluations against conventional FA methods and leading metaheuristic algorithms—such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—demonstrate the superior performance of the proposed framework. The method achieves a remarkable 3.98% reduction in average path cost over the Quadratic Polynomial technique and a 2.67% improvement over the Cubic Polynomial Equation-based approach, underscoring its effectiveness in delivering computationally efficient and dynamically optimized navigation solutions for mobile robots.