Autonomous mobile robots are widely used in many applications today, but whatever task the mobile robot has, it needs to be able to navigate in the environment. To accomplish this, pathfinding algorithms are required. This BSc thesis focuses on bug algorithms, which form a family of local pathfinding algorithms. These algorithms assume that the robot knows its current position, the goal position, and it can locally detect obstacles. Using this data, the bug algorithms navigate the robot to the goal position in the presence of static obstacles. After introducing some members of the bug algorithm family (Bug0, Bug1, Bug2, Alg1, Alg2, VisBug, DistBug, TangentBug, PointBug), the PointBug algorithm is first implemented in simulation, then on the NI Robotics Starter Kit 2.0 mobile robot equipped with a NI sbRIO-9632 single board computer. One of the advantages of PointBug is that it can be implemented using a single ultrasonic transducer. The position estimation is done with dead reckoning using the measurements of the incremental encoder mounted on the wheels. The measurements of the compass were also merged in the orientation estimation using Kalman filtering techniques. A PID controller was used to improve the path-following of the robot by keeping the robot heading close to its reference value. The results of the simulation and the implementation of the algorithm on the robot show that the PointBug algorithm is suitable for navigating indoor mobile robots of this configuration.