Unmanned ground vehicles (UGVs) have been widely used in many areas such as agriculture, mining, construction and military applications. This results from the fact that UGVs can not only be easily built and controlled, but also be featured with high mobility and handling hazardous situations in complex environments. Among the competences of UGVs, autonomous navigation is one of the most challenging problems. This is because that the success in achieving autonomous navigation depends on four factors:​ Perception, localization, cognition, and proper motion controller.

In this thesis, we introduce the realization of autonomous navigation for a two-wheeled differential ground robot under the robot operating system (ROS) environment from both the simulation and experimental perspectives. In Chapter 2, the simulation work is discussed. Firstly, the robot model is described in the unified robot description format (URDF)-based form and the working environment for the robot is simulated. Then we use the gmapping package which is one of the packages integrating simultaneous localization and mapping (SLAM) algorithm to build the map of the working environment. In addition, ROS packages including tf, move_base, amcl, etc., are used to realize the autonomous navigation. Finally, simulation results show the feasibility and effectiveness of the autonomous navigation system for the two-wheeled UGV with the ability to avoid collisions with obstacles.

In Chapter 3, we introduce the experimental studies of implementing autonomous navigation for a two-wheeled UGV. The necessary hardware peripherals on the UGV to achieve autonomous navigation are given. The process of implementation in the experiment is similar to that in simulation, however, calibration of several devices is necessary to adapt the scenario in a practical environment. Additionally, a proportional-integral-derivative (PID) controller for the robot base is used to handle the external noise during the experiment. The experimental results demonstrate the success in the implementation of autonomous navigation for the UGV in practice.