This paper presents a method for the automatic observation of unknown objects. We aim at finding the position of the targeted object and at capturing multiple views of its shape using a single eye-in-hand camera. The main goal is modeling the unknown object via 3D reconstruction before grasping and manipulating it with a robot hand. The proposed method built over a visual servo loop uses simple features to constantly gaze at the object and handles a three-step exploration with the same control structure. Images captured with the experimental setup are processed online to recover a model with millimetric accuracy.

This paper presents a new solution for solving the grasping force optimization problem, fundamental in dexterous manipulation by multifingered robotic hands. Several methods have been proposed in the literature, yielding optimal solutions, with either recursive or non linear programming techniques. However, most of them involve many computations and cannot be used online. Furthermore, they do not offer a smooth solution regarding to possible changes in the contact conditions due to finger rolling or gaiting, or in the desired resultant force to be exerted on the grasped object. The more recent ones are fast and smooth enough for real-time computation but the method we present here is faster, easier to implement and provide very satisfying results, even though the solution is sub-optimal. The method is based on the minimization of a cost function that gives an analytical solution but does not ensure by itself the satisfaction of the static frictional constraints. An associated iterative adjustment modifies this function until the internal forces enter the friction cone. The minimal solution is found within a few iterations. Force determination is therefore included in the simulation of a hybrid position/force controller to prove the effectiveness of such an approach for updating the force references during the grasped object motion.

We deal with the control of a multifingered robot hand in a dexterous manipulation task. For that purpose, we have previously proposed an efficient modular and hierarchical control scheme. This solution is different from other proposed methods since the position and force controllers are decentralized, that is, implemented at the local level of each finger. A new module is introduced here to compensate for the error caused by the deviation of the contact locations due to the rolling contacts between the fingertips and the handled object. On the basis of the contact kinematics, the "rolling contact" module updates the contact locations to modify the position and force references sent to each local finger controller in order to account for the motion of the contact points.

In this paper, a penalty approach which deals with a constrained optimization to solve the inverse kinematic of redundant robot manipulators is considered. An optimization procedure using neural networks is formulated, it produces on-line position and velocity trajectories in joint space from position and orientation trajectories in Cartesian space. This new method offers substantially better accuracy and guarantees a good minimization of a performance function subject to joint limitations while achieving the end-effector task.