The path planning algorithm is a key component in the research of mobile robots. The ant colony algorithm is indeed a relatively mature algorithm. To address the problems existing in the path planning algorithm of mobile robots, such as slow convergence speed, numerous turning points, and poor stability, an improved dynamic optimization ant colony algorithm (IDOACO) is proposed. First, heuristic information with directional guidance is introduced to enhance the purposefulness of path planning through the angle guidance factor. Secondly, an obstacle exclusion factor and safety factor are incorporated into the pseudo-random state transition probability to improve path safety. Furthermore, a multi-objective evaluation function is proposed to balance the path length and energy consumption to achieve global optimization of path planning. Finally, a dynamic obstacle avoidance adjustment module is formulated to assess and adjust the path in real time, enabling instant dynamic obstacle avoidance functionality. Simulation experiments are implemented to compare the IDOACO algorithm. Compared with the existing algorithms, experimental results show that, in a complex map environment, the IDOACO algorithm improves the average path length by approximately 4.63% and 11.78%, and the standard deviation of the convergence speed is increased by 55.21% and 66.27% respectively. Experiments show that the shortest path generated by the IDOACO algorithm not only converges faster, the number of turns is less, but also has higher stability and convergence accuracy. Then, the dynamic obstacle avoidance effect is successfully verified. Finally, the improved algorithm is applied to the ROSMASTER-X3 mobile robot, and different target points are set for actual path planning. Experimental results show that the algorithm can effectively solve the problems faced by the mobile robot in path planning, and has certain practical application value.