Exploring Multi-Objective Evolutionary Approaches for Path Planning of Autonomous Mobile Robots

In recent years, significant advancements have been witnessed in the field of mobile robot path-planning research. However, navigating through complex scenarios poses a formidable challenge for mobile robots, given the multitude of objectives they must satisfy. Traditional path-planning algorithms often struggle in such environments, highlighting the need for more sophisticated approaches. Among these, the multi-objective evolutionary optimization paradigm stands out for its ability to tackle the complexities inherent in mobile robot navigation. This study delves into the efficacy of three prominent multi-objective evolutionary approaches based on different principles, specifically tailored to address the challenges of minimizing path time and enhancing trajectory smoothness for ground robots. Through a comprehensive analysis, we explore their performance across four distinct environments, each presenting unique navigational hurdles. The comparative evaluation reveals that NSGA-II emerges as the frontrunner among the trio of algorithms, consistently delivering superior results across varied scenarios. Its adeptness in balancing conflicting objectives and generating optimized paths underscores its efficacy in real-world applications. By synthesizing empirical findings, this study sheds light on the evolving landscape of mobile robot path planning and underscores the pivotal role of multi-objective evolutionary optimization in overcoming navigational complexities.