M.Ing. / The objective of this research is to present motion planning methods for an autonomous robot. Motion planning is one of the most important issues in robotics. The goal of motion planning is to find a path from a starting position to a goal position while avoiding obstacles in the environment. The robot's environment can be static or dynamic. Motion planning problems can be addressed using either classical approaches or obstacle-avoidance approaches. The classical approaches discussed in this work are: Voronoi, Visibility graph, Cell decomposition and Potential field. The obstacle avoidance approaches discussed in this research are: Neural network, Bug Algorithms, Dynamic Window Approach, Vector field histogram, Bubble band technique and Curvature velocity techniques. In this dissertation, simulation results and experimental results are presented. In the simulation, we address the motion planning issues using points extracted from a map. Algorithms used for simulation are: Voronoi algorithm, Hopfield neural network, Potential field and A* search algorithm. The simulation results show that the approaches used are effective and can be applied to real robots to solve motion planning problems. In the experiment, the Dynamic Window Approach (DWA) is used for obstacle-avoidance, a Pioneer robot explores the environment using an open source system, ROS (Robot Operating System). The experiment proved that DWA can be used to avoid obstacles in real time. keywords Motion planning, autonomous robot, optimal path problems, environment, search algorithm, classical approaches, obstacle avoidance approaches, exploration.