This paper introduces a robust, adaptive and learning approach, called a nonlinear Log-Artificial-Bee-Colony in (‘a’,‘b’) color space (Log-ab), for the recognition of colored markers. Log-ab optimizes the Recognition Performance Index (RPI) of the marker’s templates by using the proposed on-line Bee-colony method for the purpose of adapting in the varied light environment. Furthermore, Log-ab guides a multidirectional robot accurately to move on a desired path in the dynamic light’s disturbance by using Log-ab controller. Simultaneously, the proposed multidirectional robot with Kinect performs pattern recognition as well as measures the depth and orientation of a marker quite precisely. Then, for verification of the effectiveness of dynamic (‘a’,‘b’) color space, the results of Signal to Noise (S/N) run as well as these results show the advantages of the proposed method over the existing color-based methods. Finally, Tracking Success Rate (TSR) of robot for a specific colorful marker shows the robustness of the proposed case as compared with the popular Scale Invariant Feature Transform (SIFT) and Phase-Only Correlation method (POC).