A Neural Network-Based Navigation Approach for Autonomous Mobile Robot Systems

Abstract

A mobile robot is a futuristic technology that is changing the industry of automobiles as well as boosting the operations of on-demand services and applications. The navigation capability of mobile robots is a crucial task and one of the complex processes that guarantees moving from a starting position to a destination. To prevent any potential incidents or accidents, navigation must focus on the obstacle avoidance issue. This paper considers the navigation scenario of a mobile robot with a finite number of motion types without global environmental information. In addition, appropriate human decisions on motion types were collected in situations involving various obstacle features, and the corresponding environmental information was also recorded with the human decisions to establish a database. Further, an algorithm is proposed to train a neural network model via supervising learning using the collected data to replicate the human decision-making process under the same navigation scenario. The performance of the neural network-based decision-making method was cross-validated using both training and testing data to show an accuracy level close to 90%. In addition, the trained neural network model was installed on a virtual mobile robot within a mobile robot navigation simulator to interact with the environment and to make the decisions, and the results showed the effectiveness and efficacy of the proposed algorithm.