# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 2 Preliminary
### Lstm-Dqn-Apr  
In addressing the problem of UAV trajectory prediction and obstacle avoidance in complex environments, traditional DQN and APF methods often face challenges such as trajectory prediction delays, local minima traps, and insufficient safety in policy exploration due to a lack of temporal prediction capabilities and adaptability to dynamic environments. This paper introduces the LSTM prediction algorithm, which enables more accurate real-time trajectory predictions for UAVs, helping to avoid premature convergence or falling into local optima caused by environmental changes. The LSTM-DQN-APF integrated algorithm model is shown in Figure 2. Its core architecture collaborates through a \"temporal prediction--global decision-making--physical constraints\" mechanism, constructing a closed-loop obstacle avoidance system for adaptive complex urban scenarios. Firstly, the LSTM temporal prediction module, based on multi-source sensor data, uses the temporal modeling capabilities of bidirectional long short-term memory networks to precisely capture the motion patterns of dynamic obstacles and predict their future trajectories, effectively solving the prediction delay issue caused by environmental dynamics in traditional APF algorithms. Secondly, DQN, driven by dynamic reward functions and utilizing experience replay and target network separation mechanisms, generates global optimal path strategies, overcoming the local minima dilemma caused by potential field gradient conflicts in APF. At the same time, the physical potential field constraints of APF are introduced as safety boundaries to ensure the obstacle avoidance reliability of DQN during the policy exploration process. The collaborative interaction of these three components constructs a closed-loop system that combines environmental adaptability, decision-making intelligence, and safety constraints, significantly improving the obstacle avoidance performance of UAVs in complex urban environments.  
Based on the above model, Figure 3 illustrates the data flow of LSTM-DQN-APF. The specific process is as follows:  
Figure 3: LSTM-DQN-APF FlowchartIn.  
Figure 2: The trajectory prediction model of quadrotor UAV based on LSTM.  
For the UAV dynamic obstacle avoidance path planning algorithm based on LSTM-DQN-APF, we first need to define the inputs and outputs, as well as set the initial conditions. The following is a detailed description of Algorithm 1.  
```
Input: Virtual sensor data, real sensor data (timestamp, raw coordinates, velocity, obstacle point cloud); Output: UAV action decisions (heading angle, velocity), updated environmental state; Initialize twin platform and physical UAV entity; Initialize extended Kalman filter, LSTM network, DQN policy network, and APF potential field parameters; while goal point is not reached do  Obtain virtual and real sensor data, and perform multi-source data fusion using the extended Kalman filter;  LSTM sequence prediction, input current pre-processed data and previous feedback state, output predicted trajectory;  Based on the current obstacle distribution, perform APF potential field calculation; Input LSTM and APF outputs into DQN for decision making; Action execution and feedback  Execute action, update UAV state (new coordinates, velocity);  Collect environmental feedback (new obstacle distribution, reward signal); if dynamic obstacle avoidance is triggered then  Update hidden state using LSTM;  Backpropagate to adjust DQN network parameters; else  Maintain current policy network; end if end while
```  
**Algorithm 1** UAV Dynamic Obstacle Avoidance Path Planning Based on LSTM-DQN-APF  
Through this algorithm, LSTM can capture temporal information, DQN can make dynamic decisions, and the APF potential field provides effective obstacle avoidance capabilities. The combination of the three achieves real-time obstacle avoidance and path planning for UAVs in dynamic environments.