Cooperative Object Transport and Assembly: Pose/Force Control by Visual-Tactile Feedback

This letter proposes a control framework for dual-arm robotic manipulation of a common workpiece, with the objective of tracking a reference trajectory for the object while minimizing the internal stress on it using pose/force controllers. The visual-tactile observations provided by the individual robots are fused, using a discrete-time Extended Kalman Filter designed on the$SE(3)$manifold, to online calibrate their relative pose and estimate both the object pose and the internal forces at the same time. This algorithm ensures consistency of the estimated quantities evolving in$SE(3)$without any simplifying assumptions. The cooperative robots exhibit a high degree of redundancy owing to the suitable modulation of grasp forces enabling dexterous in-hand manipulation actions such as pivoting or sliding. The redundant degrees of freedom can easily be handled at kinematic level, owing to the soft contact suitably modeled with an elastic displacement in$SE(3)$to keep kinetostatic consistency with the robot motions taking place in this manifold. Experimental results on a team of two heterogeneous robots demonstrate the effectiveness of the proposed method.