Abstract

This research presents a comprehensive analysis of path planning and optimization techniques in mobile robotics, focusing on both classical algorithms and modern intelligent approaches. The study systematically reviews fundamental methods such as Dijkstra’s algorithm, the A* search algorithm, and artificial potential fields, together with evolutionary optimization approaches including genetic algorithms and swarm intelligence. It also explores the application of machine learning and deep reinforcement learning models that allow robots to adapt dynamically to complex and changing environments. The comparative evaluation highlights the strengths, weaknesses, and suitable application areas of each approach across scenarios involving obstacle avoidance, energy efficiency, real time adaptability, and multi robot coordination. Particular attention is given to the challenges of uncertain and dynamic environments, computational scalability, and sensor noise, which continue to limit the performance of autonomous navigation systems. By consolidating current advancements and emerging trends, this study provides a structured overview and critical synthesis of existing methodologies, offering a valuable reference for researchers, engineers, and practitioners. It also identifies important research gaps in intelligent hybrid planning, context aware learning and energy constrained mobility, outlining promising directions for the future development of autonomous robotic navigation systems.