This paper reviews key obstacle avoidance and path planning methods for autonomous navigation of mobile robots. It discusses both classic and modern algorithms, including Dijkstra, Floyd-Warshall, Bellman-Ford, Artificial Potential Field (APF), Bug algorithms, Follow the Gap Method (FGM), Vector Field Histogram (VFH), Cell Decomposition (CD), Probabilistic Roadmap Method (PRM), and Rapidly Exploring Random Tree (RRT). Heuristic algorithms such as A*, Fuzzy Logic (FL), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Particle Swarm Optimization (PSO) are also covered. The paper analyzes the advantages, limitations, and applications of these algorithms, highlighting current research directions in obstacle avoidance robotics. It emphasizes the importance of obstacle avoidance in ensuring safe and efficient navigation for autonomous robots. The review also discusses the use of predictive methods and deep learning strategies in path planning. The paper aims to provide a comprehensive overview of the current state and future prospects of obstacle avoidance algorithms in robotics applications.This paper reviews key obstacle avoidance and path planning methods for autonomous navigation of mobile robots. It discusses both classic and modern algorithms, including Dijkstra, Floyd-Warshall, Bellman-Ford, Artificial Potential Field (APF), Bug algorithms, Follow the Gap Method (FGM), Vector Field Histogram (VFH), Cell Decomposition (CD), Probabilistic Roadmap Method (PRM), and Rapidly Exploring Random Tree (RRT). Heuristic algorithms such as A*, Fuzzy Logic (FL), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Particle Swarm Optimization (PSO) are also covered. The paper analyzes the advantages, limitations, and applications of these algorithms, highlighting current research directions in obstacle avoidance robotics. It emphasizes the importance of obstacle avoidance in ensuring safe and efficient navigation for autonomous robots. The review also discusses the use of predictive methods and deep learning strategies in path planning. The paper aims to provide a comprehensive overview of the current state and future prospects of obstacle avoidance algorithms in robotics applications.