# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
### Literature Review and Contribution
However, in a dynamic environment, a large amount of dynamically changing information is collected by sensors, and there are no relevant training samples to help clustering algorithms complete their training; therefore, it is difficult to complete the clustering work with traditional clustering algorithms, such as hierarchy-based clustering algorithms, k-means, etc. [15]. In this paper, we take advantage of the properties of a Kohonen network of automatic environment recognition and automatic classification of environmental information (a Kohonen network is a self-organizing mapping network that works via unsupervised learning) to optimize the Q-learning algorithm [16]. Automatically identifying and classifying environmental information can shrink Q-tables and turn complex 3D spaces into streamlined mapping networks [17]. However, in a dynamic environment, a local path planning algorithm avoids dynamic obstacles by sacrificing time and only performs local instead of global searches, which leads to an unsatisfactory effect of multi-AGV path planning. Therefore, many scholars have combined global path planning algorithms and local path planning algorithms to obtain improved planning results [18]. [PERSON] [19] adopted a three-layer control architecture model and proposed an obstacle avoidance algorithm integrating a global planning algorithm and the artificial potential method to effectively accomplish obstacle avoidance tasks in a dynamic environment. The artificial potential field method used in this method is a feedback control strategy that is robust to control sensing errors; however, it suffers from a local minimum problem and is therefore not guaranteed to find a locally optimal path. [PERSON] [20] built a six-layer structured network containing three main components: path planning, information collection, and autonomous navigation. [PERSON]'s network involves two layers of path planning: static planning and dynamic planning. Dynamic planning can be subdivided into costing and path optimization; two layers of information collection: intelligent detection and environmental information collection and fusion; two layers of autonomous navigation: localization and guidance, and path selection. This method requires high-precision sensors to collect environmental information, and the sensor accuracy of common AGVs is not high. In addition, this method uses a BP neural network to classify environmental information, which leads to unsatisfactory classification results. The above methods only combine global and local algorithms but lack consideration of the actual environment, such as road conditions, sensor accuracy of AGVs, etc. More critically, they lack scheduling between AGVs and analysis of the current congestion, so they are not suitable for the actual working environment. We use road conditions and congestion as constraints to improve the genetic algorithm (T-GA) and will evaluate the congestion of the working environment for the scheduling of multiple AGVs. Finally, we combine the improved genetic algorithm with the K-L algorithm to propose a path planning method for multiple AGVs that is suitable for real dynamic working environments. To further improve the efficiency of AGVs, many scholars have combined scheduling and path planning. For example, [PERSON] designed a Flexsim simulation model of AGVs in a manufacturing plant, which can be used to study the shortest motion path and lowest cost of AGVs [21]. [PERSON] considered a method to improve the operational efficiency of the system, which combines a task assignment strategy based on a genetic algorithm (GA) and a path planning strategy based on reinforcement learning (RL) [22]. The above methods all obtain task assignment schemes through scheduling first and then perform path planning, rarely performing both tasks simultaneously. Therefore, if the work environment changes, the new task assignment scheme cannot be instantly fed back to the path planning.