## 1 Introduction
In each framework, a decision algorithm based on variant deep Q-learning (DQN) was proposed to learn and explore strategies from local maps in [28]. An actor-critic model was trained to learn navigation policy, and expert knowledge was utilized to generate subgoals in [29]. In [30], a heuristic dynamic programming method was proposed to demonstrate a Markov decision process during a maze navigation task, which included a goal network to model the reward. The research in [31] developed an obstacle avoidance method using a fuzzy neural network for wheeled mobile robots, which reduced the oscillation during obstacle avoidance and achieved a smooth path. In [32], symbolic spatial information was employed to assist robots to move in an unseen scene, which achieved good performance. Note that the aforementioned methods all incorporated a relatively heavy real-time computation load as a key component in their design framework, and it was difficult to set new plans when receiving instantaneously a large amount of information. In [33], artificial and natural landmark recognition-based simultaneous localization and mapping (SLAM) provided absolute position feedback in real time to complete the task of robot localization and modeling. For motion planning, a novel quadrupole potential field (QPF) approach was used to realize collision-free path planning. However, the modeling environment based on SLAM was time-consuming and had no ability to cope with real-time changes in scenarios, which increased the difficulty of navigation without global environmental information. To overcome the real-time computation issue in navigation problems, this paper formulates a mobile robot navigation problem within a 2D plane with various shapes of obstacles. It divides the motion behaviors of a given autonomous mobile robot system into a finite number of motion types, and collects the environmental information (system inputs) as well as appropriate decisions (labels) of these motion types made by human users at various scenarios with random generated obstacles. These collected data are further exploited to train a neural network model that is employed to replicate the decision-making behavior of a human being. The trained model only responds to the real-time detected environmental information and does not have to perform any optimization procedures. Todetermine the feasibility of this neural network based method, a simulator was established in Python to present an exemplary mobile robot navigation task. The tuning procedures using supervised learning were made on the structure of the neural network and the parameters of the training algorithm to guarantee the high accuracy of this method with respect to the training and testing data sets. To validate its performance, the trained model was applied to the control system of the mobile robots in the simulator multiple times. The resulting paths confirmed its ability to handle navigation tasks and achieve obstacle avoidance as well. The scientific and technical contributions of this paper are highlighted as follows:  
* A autonomous mobile robot navigation problem was formulated with a finite number of motion types, requirements on obstacle avoidance in 2D space, and no global environmental information. * An implementation algorithm is provided on how to process the obtained data and train a neural network model using a supervised learning method to approach the human decision-making process. * A simulator was established in the Python programming environment to model each step of an exemplary mobile robot navigation task with visualization outcomes, and the human decision data were collected under the same navigation scenario of the simulator. * The proposed algorithm used the data collected from the human decision-making process under the same navigation scenario to train a neural network model to achieve high estimation accuracy rates.