## 1 Introduction  
The navigation problems of mobile robots require a method that can successfully lead a robot to its target destination, and they are of key significance in industry [1]. For example, in an intelligent warehouse, the goods are frequently carried or transported by mobile robots from one position to another for storage management purposes. In such a navigation problem, the feature of obstacle avoidance must be considered with highest priority in the navigation algorithm design process, otherwise there may be serious accidents, such as a collision between two mobile robots or another object. To address these kinds of navigation problems, previous research has used various methods, such as convex optimization [2], virtual and behavioral structures [3], model predictive control [4], and reinforcement learning [5], to achieve an obstacle avoidance design target in practical applications. For more details, please see the overview of navigation as well as obstacle avoidance problems in [6; 7; 8]. The existing methods for navigation tasks may require a high computation load to compute the desired path for the mobile robots or ensure they follow a predefined route [9]. However, these requirements are too high for the performance of existing equipment. For instance, the microprocessors installed on the mobile robots may not have an efficient enough computation load to determine the correct path in real time. In addition, the limited choices of the predefined routes do not actually encourage cooperation between multiple mobile robots, i.e., a quick vehicle behind a slower vehicle has to queue and wait in the same route, which is not efficient. Although the work in [10; 11; 12] has attempted to overcome these concerns, the proposed algorithms were adhoc, and their applications were restricted to the specific navigation tasks with convex shaped static obstacles or global environmental information. An artificial potential field is considered an efficient method for motion planning, but it runs the risk of falling into a local optimum. This paper intends to deal with the class of navigation problems using obtained human decision data from the aspect of artificial intelligence. As pointed out in [13], artificial intelligence is considered as a technique aiming at replicating human behavior by making inferences according to historical learned data. It usually establishes a neural network model and tunes the parameters within this model using the obtained realistic data, so that the trained model can provide useful information for the users [14]. As explained in [15], this technique is particularly strong at making intelligent decisions for certain design problems with variability, uncertainty, complexity, and fuzziness [16]. This advantage enables its applications to many research fields, such as image recognition [17; 18; 19], data analysis [20], system identification [21; 22; 23], and mechanical fault diagnosis [24]. Note that most of the applications mentioned above deal with data using artificial intelligence, whose structures and labels are both sparse. However, the data labels of the navigation problem are not purely sparse, since one specific occasion can have multiple potential labels. To solve this specific class of navigation problems using artificial intelligence, some work has already been completed. The work in [25] employed the Q-learning method to interact with a dynamic environment and limited the state number of its state space to fulfill a navigation algorithm. In addition, it was reported in [26] that neural network-based reinforcement learning was used to handle the navigation tasks with both static and dynamic obstacles in an uncertain workspace. Deep reinforcement learning to solve navigation problems is increasingly favored by researchers and has progressed since 2016 [27]. The exploration process is decomposed into decision-making, planning, and mapping modules to increase the modularity of the robot system.