The purpose of this project is to implement obstacle avoidance algorithms to drive the articulated vehicle autonomously in an unknown environment, which is simulated by AgX Dynamics™ simulation software and controlled by Matlab® programming software. Three driving modes are developed for driving the vehicle (Manual, Semi-autonomous and Autonomous) in this project. Path tracking algorithms and obstacle avoidance algorithms are implemented to navigate the vehicle. A GUI was built and used for the manual driving mode in this project. The semi-autonomous mode checked different cases: change lanes, U-turn, following a line, following a path and figure 8 course. The autonomous mode is implemented to drive the articulated vehicle in an unknown environment with moving to a pose path tracking algorithm and VFH+ obstacle avoidance algorithm. Thus, the simulation model and VFH+ obstacle avoidance algorithm seems to be working fine and still can be improved for the autonomous vehicle. The result of this project showed a good performance of the simulation model. Moreover, this simulation software helps to minimize the cost of the articulated vehicle since all tests are in the simulation rather than in the reality.

This paper addresses the problem of mobile robot localization based on current 2D and 3D data and previous records. Focusing on practical loop detection in the trajectory of a robot. The objective of this work was to evaluate current methods of image processing and depth data for issues of localization in environment. This work uses Bag of Words for 2D data and environment of point cloud with Viewpoint Feature Histogram for 3D data. Designed system was implemented and evaluated.