| The potential field control model provides a powerful, yet mathematically simple method to motivate a mobile robot. It is a local control method based on simple physical principles. This allows the robot to proceed toward a target while avoiding obstacles following a naturally smooth path. It ensures the robot will behave according to Newtonian principles that have been studied for centuries. The potential field model has several weaknesses however. Most of the weaknesses can be lessened through careful modification of the potential field but the success of this approach is limited. For further improvements, global methods must be employed. These methods use previous knowledge of the environment to find a safe and efficient path. This thesis demonstrates that the strategic addition of a negative velocity feedback to the original potential field model will reduce or eliminate oscillations and eliminate the stall issue. It also shows that the proper approach to the use and placement of "false" obstacles improves operation further with little overhead. From there, it examines particle swarm, genetic algorithm, ant colony global and MAKLINK approaches and their combinations. Finally, it proposes a combined approach to implement on large or resource-sensitive robots. |
