This paper deals with the dynamic modeling and identification of an electrically driven underwater robot manipulator. The proposed study includes the dynamic modeling of the actuators of the arm as well as the identification of the parameters of the model. The proposed method deals with the specific case of heterogeneously actuated arms, namely arms with actuators behaving differently for each joint, being considered at the kinematic level. Indeed, we show how to estimate the arms parameters when some of their revolute joints are directly actuated by geared motors, while the others are actuated by linear actuators. A minimum set of identifiable parameters is determined, and adequate excitation trajectories are generated and used in the identification procedure. Realtime experimental validation on the manipulator arms of Ifremer's HROV (Hybrid Remotely Operated Vehicle) Ariane underwater vehicle demonstrates that the proposed method improves the estimation of the dynamic model.

The paper discusses the employment of different sources of information to support robotic assembly operations. During component interaction, part of the wrist sensed force and torque information is found to be geometrically dependent. This enables the real-time sensorial data retrieved from the assembly scene to be combined with the information on the geometry of the component and the history of the insertion itself. As a result, an intelligent control architecture is developed to perform simple peg-hole assembly operations emphasising the aspects which relate to learning an appropriate state-action mapping without requiring an a priori defined set of manipulative skills. A real time peg in hole experiment involving a PUMA 761 industrial manipulator is detailed to support the theoretical results.