Abstract. The use of robots had increased widely in various fields, such as air transport systems, search and rescue, and agriculture, necessitating the need for path planning and obstacle avoidance systems to ensure safe and autonomous navigation toward the intended goal. Many Path-planning techniques are used to guide the mobile robot toward its goal with an optimized path and time. Among these techniques, the Artificial Potential Field (APF) algorithm stands out as one of the effective approaches, capable of operating in both static and dynamic environments to achieve optimal path planning. The APF is built by fusing two forces that attract the robot toward a goal location and repulsive forces that repel the robot away from the obstacle. Despite its effectiveness, the APF algorithm faces a major challenge, which is falling into a local-minimum issue. This paper presents a hybrid approach that combines the modified APF algorithm global optimization capabilities with the A-Star path-planning technique's real-time adaptability to overcome traditional APF local minimum issues. The transition back and forth between the two algorithms where carried out by a manager that can determine the adequate algorithm to be used instantaneously. Several experiments are presented to demonstrate the hybrid algorithm's effectiveness in various environments. The results show that the enhanced APF reach an optimal path to goal 50% faster compared to A-star, and managed to get out of local minimum compared to traditional APF and find a path shorter than A-star.