The local minimum problem caused by path planning of potential field has received much more attention. To cope with this issue, in this paper, the relationship between the potential force field and the velocity direction is analyzed when the robot has been trapped in the local minimum, and an obstacle avoidance method based on the motion cumulative angle is proposed. The internal and external turning angles of the robot are taken to appraise the relationship between the direction and angle of the robot. Based on the angle cumulative, the “key reset point” is defined. By refreshing its own position and resetting its cumulative angle, the complex environment is simplified and the path planning is realized in an unknown environment. Simulation studies indicate that proper switching conditions for each state are designed to guarantee the orders of the state switching and smooth operation, and therefore the robot can improve the flexibility and reliability of decision. Compared to some existing methods, the method has the advantages of shorter plan path and higher efficiency. The feasibility of this method is verified by a path planning experiment on the self-made mobile robot. These results show that the proposed algorithm is suitable for the first time through the unknown complex environment and the scenario of path planning without mapping.