# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 1 Introduction  
As artificial intelligence and robotics rapidly advance, mobile robots are being increasingly utilized in sectors such as industrial manufacturing, medical science, social science, agriculture, education, games, and space research [1]. Autonomous navigation [2] technology, as the core of mobile robots for achieving autonomous mobility and intelligent decision-making, directly affects the work efficiency and adaptability of robots. Autonomous navigation requires the robot to not only avoid obstacles in known or unknown environments, but also dynamically adjust the path to adapt to environmental changes, thereby ensuring efficient completion and safety of the task. Effective autonomous navigation technology can not only improve the autonomy of robot task execution, but also reduce human intervention and reduce operating costs.  
It means that the mobile robot autonomously designs the path from the starting point to the end point, with the shortest distance, the shortest time consumed, and the safest and most collision-free path [3]. Path planning can be categorized into offline learning and online learning methods [4]. Offline learning refers to the process of pre-training a model before the robot executes its tasks, and then applying this model during actual operation. The advantage of this approach is that it allows for extensive optimization and adjustment using large amounts of data during the training phase. Typical offline learning path planning algorithms include the A* algorithm and Dijkstra's algorithm, amongothers [5]. Online learning, on the other hand, involves real-time learning and updating of the model as the robot performs its tasks. The advantage of online learning lies in its ability to dynamically adapt to environmental changes, thereby enhancing the flexibility and robustness of path planning. Typical online learning path planning algorithms include the Artificial Potential Field (APF) [6] and Deep Reinforcement Learning (DRL) [7]. For instance, DQN approximates the Q-value function using a Deep Neural Network, enabling effective online path planning in high-dimensional state spaces [8].  
Despite the significant progress in the application of DRL in path planning [9], such as DQN, Double DQN [10] and Duel DQN [11], the existing methods still face some problems. First, the traditional DQN converges slowly in high-dimensional state space, which makes it difficult to adapt to the dynamic environment quickly. Second, the stochastic experience playback mechanism is difficult to effectively utilize the key experience, which affects the quality of strategy learning. Moreover, in complex environments, the reward function has limited guidance for strategy learning, which easily leads to instability.  
Aiming at the above problems, this paper proposes an improved deep reinforcement learning algorithm, \"PER-D2 MQN\", whose innovativeness is mainly reflected in the following aspects:  
1. The PER mechanism is introduced to optimize the sample selection strategy, prioritize the use of key experiences, and accelerate the learning process;
2. Adopting the Duel DQN structure to improve strategy stability and learning efficiency by decomposing the value function and advantage function;
3. Drawing on [PERSON]'s idea of reinforcement learning, a regularized \"logarithmic strategy\" is added to enhance the robustness of the reward signal and further improve the performance and stability of the algorithm.  
The subsequent structure of this article is as follows: Section 2 introduces \"Related work\" and demonstrates the application of DQN algorithm in path planning; Section 3 introduces \"Implementation based on PER-D2 MQN algorithm\"; Section 4 introduces \"Environment design\", including action design of turtlebot3 robot, state design and reward function; Section 5 introduces \"Experimental analysis\", which proves the superiority of the PER-D2 MQN algorithm; and Section 6 is \"Conclusion\", which explains the shortcomings of this study and future work.