This survey paper presents a comprehensive review of robotics path planning and obstacle avoidance using soft computing techniques. Path planning is a fundamental problem in autonomous robotics, requiring the determination of an optimal or near-optimal path from a start point to a target while avoiding static and dynamic obstacles. Traditional deterministic approaches often struggle in uncertain and complex environments, motivating the adoption of soft computing methods. Soft computing techniques, including fuzzy logic, genetic algorithms, particle swarm optimization, ant colony optimization, and neural networks, offer robust and adaptive solutions by handling imprecision, uncertainty, and nonlinearities effectively. These methods enable robots to make intelligent decisions in real-time scenarios and improve navigation efficiency in dynamic environments. The survey analyses the working principles, advantages, and limitations of each technique, along with their applications in mobile robots, autonomous vehicles, and industrial automation. Furthermore, the paper highlights recent advancements in hybrid approaches that combine multiple soft computing methods to enhance performance, convergence speed, and accuracy. Challenges such as computational complexity, scalability, and real-time implementation are also discussed. The study concludes by identifying future research directions, emphasizing the integration of machine learning and optimization techniques to develop more efficient and reliable autonomous navigation systems.

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