Online data-driven fuzzy clustering with applications to real-time robotic tracking

Robotic target tracking has been used in a variety of applications. Due to limited sampling rate, sensory characteristics and processing delays, an important issue in such systems is to extrapolate ahead the trajectory (position, orientation, velocity, and/or acceleration) of moving targets from past observations. This paper introduces a novel online data-driven fuzzy clustering algorithm that is based on the Maximum Entropy Principle for this particular task. In this algorithm, the fuzzy inference mechanism is extracted automatically from observed data without human help, which thus eliminates the necessity of expert's knowledge and a priori information on moving targets, as required by most traditional techniques. This algorithm does not require training, which enables it to work in a completely online fashion. Another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time. In the meantime, the introduced algorithm is able to adapt quickly to the dynamics of moving targets. All these desired features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking. Simulation results show the effectiveness and efficiency of the presented algorithm.