Short term motion prediction for moving objects in a real life Robotic navigation environment involves uncertainty and temporal validity of the results.Prediction of accurate position of a moving object and responding with quick action are the main objectives of Robot motion planning. This paper proposes a novel algorithm for short term motion prediction involving a fuzzy based predictor. Because of the multi valued nature of the fuzzy logic, this approach enjoys high robustness in dealing with noisy and uncertain data. The knowledge captured by the Rulebase has been optimized by defining directional space. In the proposed work the predictor has been evaluated with three well known defuzzification techniques. Based on the analysis of results, it has been found that Mean Of Maximum defuzzification technique has lower response time and better accuracy. The predictor is tested for various motion patterns in real life environment.