<p>The conventional artificial potential field (APF) method is commonly utilized in path planning for obstacle avoidance in mobile robots. However, it often encounters issues such as unreachable targets and getting stuck in local minima. To address the problem of unreachable targets, this study introduces angle and distance variable factors into the repulsive potential field. This modification enables the mobile robot to reach the target point even when obstacles are present nearby. To tackle the local minima problem, an enhanced artificial potential field method incorporating the firefly algorithm (FA) is proposed as an extension of the traditional APF algorithm. In cases where the mobile robot’s path planning becomes trapped in a local minimum, the current position serves as the starting point for the FA algorithm, which then identifies the optimal point among randomly generated points in its vicinity, facilitating the robot’s escape from the local minimum region. The efficacy of this approach is validated through MATLAB simulations involving randomly positioned obstacles and comparative analyses with diverse algorithms. Subsequently, experiments conducted in the Gazebo environment further confirm the method’s effectiveness.</p>