To address the challenges of insufficient safety and poor tracking accuracy faced by coal mine inspection robots when conducting path planning in complex and dynamic underground tunnel environments, this paper proposes a path planning method that integrates an improved A* global planning algorithm with a fuzzy PID motion control. By introducing an obstacle cost term and dynamic weighting strategy into the cost function of the traditional A* algorithm, the efficiency and safety of global path planning are enhanced. The initial path is smoothed using B-spline curves to make it more compliant with the kinematic constraints of the robot, thereby improving its executability and trajectory smoothness. A fuzzy PID controller based on the robot′s kinematic model is designed to replace the traditional PID controller. Through fuzzy control, the PID parameters are adaptively adjusted to achieve high-precision and high-stability tracking control of the smoothed global path, effectively coupling the linear and angular velocity control. The simulation results of MATLAB and ROS Gazebo show that the improved A* algorithm reduces the number of search nodes by approximately 65%, and the B-spline processing significantly improves the path smoothness. Compared with the traditional PID, the fuzzy model PID controller performs better in terms of path tracking accuracy and stability. The maximum lateral error range is within ±0.05 meters, and the maximum heading error is controlled within ±0.2 radians. This method significantly improves the path planning and tracking performance of coal mine inspection robots.