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We propose a new method for real time obstacle avoidance using visual information. It is based on our on-going research on vector fields and potential functions associated to successful motion planning algorithms. Specifically, this paper addresses the avoidance of dynamic obstacles which have the property of producing instabilities in the navigation vector field associated to a planning algorithm. We show how avoidance maneuvers can be seen as a reaction to potential field instabilities measured by the moving robot. The paper briefly summarizes the analytical derivation of this approach, and discusses the results of planning experiments carried out using a Nomad200 robot with a landmark-based planning and navigation systems.