Abstract:

The paper proposes a method for visual servo control of nonholonomic robots with unknown dynamics using images captured by uncalibrated cameras. The method learns the transition dynamics of the robot directly in visual feature space and linearizes it successively in order to compute controls. Experiments both in simulation and on a real testbed using a unicycle-type mobile robot demonstrate that the use of planning and trajectory stabilization algorithms based on differential dynamic programming is much more effective in handling nonholonomic constraints in executing difficult maneuvers, such as parallel parking, than more traditional visual servoing schemes that linearize the learned dynamics around a single point. The ability of the proposed method to control nonholonomic robots without manually calibrating cameras and identifying robot dynamics could potentially significantly lower the cost of deployment of autonomous mobile robots at scale.