Abstract
This paper presents a comprehensive survey of path planning and obstacle avoidance techniques in mobile robotics, addressing their theoretical foundations, algorithmic developments, and practical implementations. The study categorizes path planning strategies into classical, sampling-based, optimization-based, and learning-based approaches, highlighting their respective strengths, limitations, and applicability across different environments. Obstacle avoidance methods are similarly examined through reactive, predictive, and learning-driven paradigms, with an emphasis on sensor technologies and real-time decision-making. Integrated systems that combine global and local planning, hierarchical control architectures, and embedded execution frameworks are analyzed to demonstrate how contemporary mobile robots navigate safely and efficiently in complex, dynamic settings. Case studies, including the Robot Operating System (ROS) Navigation Stack, delivery robots, and robotic vacuums, are used to illustrate real-world deployments. Furthermore, the paper identifies ongoing challenges and open research questions related to planning under uncertainty, real-time adaptability, human-aware navigation, multi-robot coordination, and generalization through transfer learning. The discussion is supported by figures and tables summarizing algorithmic trade-offs and system architectures. This survey aims to provide researchers and practitioners with a clear taxonomy, comparative evaluation, and forward-looking insights that will inform the development of more robust, adaptive, and intelligent navigation systems in the next generation of autonomous mobile robots.