This paper presents a framework that integrates vector field based motion planning techniques with an optimal path planner. The main motivation for this integration is the solution of UAVs' motion planning problems that are easily and intuitively solved using vector fields, but are very difficult to be even posed as optimal motion planning problems, mainly due to the lack of clear cost functions. Examples of such problems include the ones where a goal configuration is not defined, such as circulation of curves, loitering and road following. While several vector field methodologies were proposed to solve these tasks, they are susceptible to failures in the presence of previously unmodeled obstacles, including no-fly zones specified during the flight. Our framework uses a vector field as a high level specification of a task and an optimal motion planner (in our case RRT¿¿¿) as a local, on-line planner that generates paths that follow the vector field, but also consider the new obstacles encountered by the vehicle during the flight. A series of simulations illustrate and validate the proposed methodology. One of these simulations considers a rotorcraft UAV equipped with a spinning laser patrolling an urban area in the presence of unmodeled obstacles and no-fly zones.
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