# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
This algorithm improves the issues of unreachable goals, local minima, and real-time performance of UAVs by introducing adaptive learning rates, adjusting exploration rates, and designing attraction and repulsion functions, among other methods. Additionally, this paper utilizes a digital twin platform [21] as a sample generator to compensate for the shortcomings of training in real environments, thereby improving the accuracy of path planning. The technical validation of UAVs is typically conducted through initial simulations, but pure simulation environments have low credibility [22; 23]. By combining virtual and real-world validation [24; 25; 26], the digital twin system can comprehensively assess the UAV's path planning efficiency and obstacle avoidance capability in complex urban environments, ensuring the successful completion of tasks. Furthermore, based on the aforementioned research, this paper verifies the LSTM-DQN-APF algorithm using the digital twin system. The innovations of this paper are as follows:  
1. By combining DQN and APF, and through adjusting the exploration rate, designing reward functions, and optimizing strategies, the issues of target inaccessibility, local minima, and the difficulty of real-time updates in dynamic environments for UAVs are effectively addressed, improving the algorithm's effectiveness. 2. To enhance the UAV's ability to respond to sudden dynamic threats in complex urban environments, this paper proposes a twin-enabled LSTM-DQN-APF algorithm. The twin platform, driven by real-time data interaction and real-world data, enables the UAV to make controllable and predictable decisions in complex environments. Additionally, by incorporating an adaptive learning rate mechanism, the algorithm avoids premature convergence and local optimality issues, thereby improving prediction accuracy and trajectory planning robustness. 3. Given that conducting real-world tests in urban environments is costly and the data samples from real scenarios are limited, this paper integrates real-world data in real-time based on a twin platform, using it as a virtual sample generator to increase sample diversity. Building on this work, the proposed LSTM-DQN-APF algorithm was validated through virtual-real integration on the constructed twin platform. Experimental results show that the algorithm can effectively address issues such as local minima, unreachable targets, poor adaptability to dynamic environments, and the issue of insufficient real-time path planning accuracy. The framework structure of this paper is as follows. The introduction of Section 1 addresses the problem of UAV path planning in complex urban environments and proposes the LSTM-DQN-APF algorithm empowered by digital twins, which improves issues such as local minima, unreachable goals, and real-time performance in traditional algorithms. Section 2 analyzes the limitations of traditional APF and DQN and constructs the LSTM-DQN-APF fusion algorithm, which enhances planning performance through LSTM time-series prediction, DQN decision making, and APF potential field constraints. Section 3 presents an improved algorithm design, enhancing adaptability and decision efficiency in dynamic environments through multi-sensor fusion, dynamic potential field function optimization, and reward mechanism design. Section 4 focuses on the modeling of the twin system and algorithm validation, building a digital twin platform that integrates virtual and physical elements. Three-tier experiments are conducted to verify the algorithm, and results show significant improvements in path planning speed and obstacle avoidance success rates in complex scenarios. Section 5 summarizes the research findings, highlighting the use of virtual-physical integrated digital twin technology to enhance the realism and credibility of the simulation environment while reducing the difficulty and experimental costs associated with transferring from simulation to the real world.