The robot navigation problem can be decomposed into the two problems of getting to a goal and avoiding obstacles. The problem of getting to a goal is a global problem in that short paths to the goal generally cannot be found using only local information. The topology of the workspace is important in finding good routes to a goal. The problem of avoiding obstacles can often be solved using only local information, but for an unpredictable environment it cannot be solved in advance because the robot needs to sense the obstacles before it can be expected to avoid them. Some have solved the navigation problem by solving these two sub-problems one after the other. A path is first found from the robot’s initial position to the goal and then the robot approximates this path as it avoids obstacles. This method is rather restrictive in that the robot is required to stay fairly close to or perhaps on a given path. This would not work well if the path found goes through a passageway which turns out to be blocked by some unforeseen obstacle. Solutions that are only local, such as those produced often by artificial potential fields, often lead the robot into local minima traps. We propose a much more flexible solution using a common tool, the artificial potential field, in a new form that we call a "hybrid artificial potential field". A hybrid potential field is obtained by combining two different kinds of artificial potential fields, a global discontinuous potential field and a local continuous potential field.