An intelligent motion planning based on fuzzy rules for the idea of artificial potential fields using analytic harmonic functions is presented. The purpose of the combination of a fuzzy controller and a robust controller is to design a realistic controller for nonlinear electromechanical systems such as an electric motor actuating an arm robot. This control algorithm is applied to the three basic navigation problems of intelligent robot systems in unstructured environments: autonomous planning, fast nonstop navigation without collision with obstacles, and dealing with structured and/or unstructured uncertainties. To achieve this degree of independence, the robot system needs a variety of sensors to be able to interact with the real world. Sonar range data is used to build a description of the robot's surroundings. The proposed approach is simple, computationally fast, and applies to whole-arm collision avoidance. The stability of the overall closed loop system is guaranteed by the Lyapunov theory. Simulation results are provided to validate the theoretical concepts, and a comparative analysis demonstrates the benefits of the proposed obstacle avoidance algorithm.