Adaptive Gap-Finding in Multi-Objective Firefly Algorithm for Mobile Robot Path Planning in Dynamic Environments

This paper tackles the essential topic of safe route planning for mobile robots traversing unpredictable settings. The primary objectives are to optimize decision variables, including robot velocity and deviation angle, to ensure smooth and safe motion while also finding the shortest distance between source and destination. To achieve this, a Multi-Objective Firefly Algorithm (MOFA) is proposed, which effectively optimizes these decision variables. The MOFA framework is tailored to concurrently minimize the path length and maximize other critical performance metrics, finding an optimal balance between speed and precision. Additionally, Context-Aware Path Planning is integrated into the MOFA algorithm, allowing the algorithm to utilize semantic understanding of the environment to inform decision-making. Path length, safety margin, smoothness, and obstacle clearance are employed to assess the quality of generated paths. The experimental findings demonstrate the superiority of the suggested technique over the most advanced alternatives.