This article provides an overview of key obstacle avoidance algorithms for autonomous navigation of mobile robots, focusing on both classic and heuristic methods. It discusses the importance of obstacle avoidance in robotics and autonomous vehicles, highlighting how these algorithms enable efficient and safe navigation. The paper covers a range of algorithms, including Dijkstra's algorithm, Floyd-Warshall, Bellman-Ford, Artificial Potential Field (APF), Bug algorithms, Vector Field Histogram (VFH), Probabilistic Roadmap Method (PRM), Rapidly Exploring Random Tree (RRT), and various heuristic approaches such as A*, Fuzzy Logic (FL), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA). Each algorithm is analyzed in detail, considering their advantages, limitations, and application areas. The article also mentions the use of deep learning strategies and predictive methods, such as Artificial Neural Networks (ANNs) and Model Predictive Control (MPC). The goal is to provide a comprehensive insight into the current state and future directions of obstacle avoidance algorithms in robotics applications.This article provides an overview of key obstacle avoidance algorithms for autonomous navigation of mobile robots, focusing on both classic and heuristic methods. It discusses the importance of obstacle avoidance in robotics and autonomous vehicles, highlighting how these algorithms enable efficient and safe navigation. The paper covers a range of algorithms, including Dijkstra's algorithm, Floyd-Warshall, Bellman-Ford, Artificial Potential Field (APF), Bug algorithms, Vector Field Histogram (VFH), Probabilistic Roadmap Method (PRM), Rapidly Exploring Random Tree (RRT), and various heuristic approaches such as A*, Fuzzy Logic (FL), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA). Each algorithm is analyzed in detail, considering their advantages, limitations, and application areas. The article also mentions the use of deep learning strategies and predictive methods, such as Artificial Neural Networks (ANNs) and Model Predictive Control (MPC). The goal is to provide a comprehensive insight into the current state and future directions of obstacle avoidance algorithms in robotics applications.