# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
Yang Yang  
School of Computer Science and Engineering Xi'an Technological University, Xi'an 710021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (M.D. ); [EMAIL_ADDRESS] (S.Q.) Yang Yang  
School of Computer Science and Engineering Xi'an Technological University, Xi'an 710021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (M.D. ); [EMAIL_ADDRESS] (S.Q.) Yang Yang  
School of Computer Science and Engineering Xi'an Technological University, Xi'an 710021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (M.D. ); [EMAIL_ADDRESS] (S.Q.) Yang Yang  
School of Computer Science and Engineering Xi'an Technological University, Xi'an 710021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (M.D. ); [EMAIL_ADDRESS] (S.Q.) Yang Yang  
School of Computer Science and Engineering Xi'an Technological University, Xi'an 710021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.  
issues in dynamic environments. For example, the GE-APF [5; 6] algorithm determines the shortest path by traversing the global map, but its discretization process leads to a lack of smoothness in the path and performs poorly in dynamic environments; the APF [7] algorithm provides smooth paths, but it can easily get stuck in local minima or make the target unreachable; the RRT algorithm may fail to find a feasible path in some complex scenarios. Heuristic path optimization algorithms, such as the A* algorithm [8] and simulated annealing algorithm [9], improve search efficiency but still encounter problems like local optima or unreachable goals. Furthermore, swarm intelligence-based biomimetic algorithms, such as genetic algorithms [10], ant colony algorithms [11], and particle swarm algorithms [12], perform well in path planning but also have limitations. Genetic algorithms are prone to premature convergence, and particle swarm algorithms often fall into local optima due to insufficient velocity adjustments. Therefore, existing algorithms struggle to address the challenges of path planning in dynamic and complex environments. In view of this, researchers have gradually shifted their focus to machine learning, particularly reinforcement learning techniques. Reinforcement learning [13; 14], through the interaction between the agent and the environment, can gradually improve path planning strategies. In situations where information is incomplete, reinforcement learning [15; 16] can help drones plan the optimal path to avoid dangerous areas. Currently, path planning methods based on reinforcement learning mostly rely on the Q-learning algorithm [17]. However, this algorithm is prone to getting stuck in local optima, has low learning and computational efficiency, and performs poorly in dynamic obstacle environments [18]. To address this issue, the DQN algorithm [19] introduces neural networks, which can handle high-dimensional state and action spaces. However, there are still issues with training efficiency in complex environments, especially when the environment exhibits temporal dependencies, where traditional algorithms struggle to effectively capture the temporal relationships. To solve these problems, digital twin technology can be incorporated into the path planning process to acquire real-time obstacle information through virtual-real data interaction. Additionally, integrating LSTM [20] into the reinforcement learning framework can leverage its ability to process sequential data, helping the agent better predict future state changes in multi-step decision making and adjust its trajectory in real time. To address the issues of local minima, unreachable goals, and poor adaptability to dynamic environments in the aforementioned path planning methods, this paper proposes the LSTM-DQN-APF algorithm.