This paper proposes a new potential field method for dynamic motion planning of mobile robots in environments where both the target and obstacles are moving. The traditional potential field method is used for path planning in stationary environments, but it is not suitable for dynamic scenarios. The authors define new potential functions that take into account both the relative positions and velocities of the robot and the target, as well as the robot and obstacles. These functions are used to generate virtual forces that guide the robot towards the target while avoiding obstacles. The new method addresses the problem of local minima, which is a common issue in potential field methods. The authors also conduct extensive simulations and hardware experiments to demonstrate the effectiveness of their approach. The key contributions of this paper include the definition of new attractive and repulsive potential functions that incorporate velocity information, the ability to achieve different tracking performances through parameter selection, and the elimination of the need for prior knowledge of obstacle trajectories. The method is applicable to both 2D and 3D environments and does not require the trajectories of obstacles to be known in advance. The paper is organized into eight sections, with the first section introducing the problem, the second section describing the problem statement, the third section presenting the new attractive potential function, the fourth section defining the repulsive potential function, the fifth section discussing local minima, and the sixth and seventh sections presenting simulation and hardware experiments. The conclusion summarizes the main findings and highlights the advantages of the proposed method.This paper proposes a new potential field method for dynamic motion planning of mobile robots in environments where both the target and obstacles are moving. The traditional potential field method is used for path planning in stationary environments, but it is not suitable for dynamic scenarios. The authors define new potential functions that take into account both the relative positions and velocities of the robot and the target, as well as the robot and obstacles. These functions are used to generate virtual forces that guide the robot towards the target while avoiding obstacles. The new method addresses the problem of local minima, which is a common issue in potential field methods. The authors also conduct extensive simulations and hardware experiments to demonstrate the effectiveness of their approach. The key contributions of this paper include the definition of new attractive and repulsive potential functions that incorporate velocity information, the ability to achieve different tracking performances through parameter selection, and the elimination of the need for prior knowledge of obstacle trajectories. The method is applicable to both 2D and 3D environments and does not require the trajectories of obstacles to be known in advance. The paper is organized into eight sections, with the first section introducing the problem, the second section describing the problem statement, the third section presenting the new attractive potential function, the fourth section defining the repulsive potential function, the fifth section discussing local minima, and the sixth and seventh sections presenting simulation and hardware experiments. The conclusion summarizes the main findings and highlights the advantages of the proposed method.