Hybrid Position and Image Based Visual Servoing for mobile robots

Visual servoing requires the target object to be in the field of view of the camera all the time. At the same time, we also want to achieve optimal path planning and controllability of the robot pose. This paper presents a new hybrid fuzzy control method for visual servoing of mobile robots to meet these requirements. IBVS (Image Based Visual Servoing) calculates the motion plan directly from the image space using the inverse image Jacobian so that the target object always stays within the field of view. In contrast, PBVS (Position Based Visual Servoing) uses an image-to-work space transform to plan an optimal pose trajectory directly in the cartesian. The proposed fuzzy control then integrates these two types of visual servoing through a warning signal indicating the target may escape the field of view. Also, we use the neural network for the prediction of the target position for a robust timely tracking of the object. Simulation and real experimental work based on MORIS, our mobile robot test bed, verify the efficacy of this approach.