In robotics, the manipulation ofa prioriunknown objects involves several steps and problems that must be carefully considered and solved by proper planning and control algorithms. For example, once suitable contact points have been computed, the control system should be able to track them in the approach phase, i.e., while the relative position/orientation of the object and the gripper of the robotic system change due to the approaching movement of the robot toward the object. This correspondence paper proposes a practical method for the tracking of grasp points in image space that is based on transferring previously computed grasp points from an initial image to subsequent ones and on the analysis of the new grasp configuration. Three different options are proposed for this transference. Experimental results show the interesting practical performance of the general procedure.

The visual tracking of grasp points is an essential operation for the execution of an approaching movement of a robot arm to an object: the grasp points are used as features for the definition of the control law. This work describes a strategy for tracking grasps on planar objects based on the use of a homography. In particular, the homography is used for transferring (translating) a grasp from a view of an object to a second one, providing in this way a correspondence in the second view. The grasp tracking procedure combines the search of a new grasp with the translation and the evaluation of that grasp between object views. Results of the proposed grasp tracking strategy are shown in case of grasps computed for a two-finger and a three-finger gripper.

The positioning of a gripper with respect to an object is one of the basic steps involved in the manipulation of that object. In particular, in a grasping task the fingers of the gripper should be placed at a set of selected grasp points belonging to the object. In this paper, a two-finger gripper and a stereo pair of cameras mounted in an eye-in-hand configuration are considered for this task. A visual servoing control system is proposed for the guidance of the positioning movement in which the grasp points are tracked in image space and used as input features for the control law. This tracking is based on the use of an invariant description of the grasp with respect to the object. The control law is restricted to control the degrees of freedom that produce movements that respect the invariance required by the grasp tracking.

Many complex systems-such as robotic systems-need a distributed solution in which a set of specialized software modules would communicate with each other to achieve global objectives. Software agents are a possible approach to this need. This paper reports the development of a general purpose agent library that eases the building of applications based on distributed agents. Such applications will be modular, flexible, extensible, location independent and fault tolerant. The paper also describes some examples of applications built with this library.

This paper introduces a distributed, scalable architecture for controlling a manufacturing cell. Normally, these cells are composed of heterogeneous parts. An architecture is proposed that tries to avoid this problem. In this architecture different components are used: virtual sensors, filters and actuators. A simple communication interface between them is defined. These components become extended components with the addition of a communication wrapper adds the capability of remote communication.

In this paper, a strategy that combines the tracking of an object and the selected grasp on it is presented. This strategy is intended to provide enough information to a control system for executing the grasp. The tracking of the object uses the ICondensation and AARM prediction algorithms to predict the current object position and shape in the image space. Using a set of quality criteria, stable grasps are searched for on the object contour and the best one is selected. The selected grasp is tracked together with the object contour in the sequence of images.

A key issue in robotics is the development of the ability to grasp unknown objects. This ability requires a grasp determination mechanism that, based on the analysis of the description of the object, determines how it can be stably grasped. In this paper, a grasp determination method is presented that computes a set of grasps that comply with the force-closure condition. Its input is a set of contours, extracted from the vision data, describing the shape of the object. The internal holes of the object are taken into account, so the algorithm can find grasps on them. The algorithm also finds expansion and squeezing grasps, which are executed by opening and closing the gripper fingers.

The construction of polygonal approximations for digital curves is a well-known technique to obtain a compact representation of them. Over the last years, a number of methods have been proposed to optimize this process, based on different criteria. In this paper, polygonal approximation is viewed as an optimization problem, and the use of a genetic algorithm is proposed as a method to find a solution that best meets a given set of requirements. This paper analyses the capability and the performance of the genetic algorithm to select the vertices of the polygons, and some advantages and drawbacks are discussed.

We present an integrated vision-guided grasping system for service robots. Our system integrates computer vision to capture the shape of the objects, online grasp determination based on that shape, and image-based control for grasp execution. Novel techniques are presented to solve the problems concerned under the imposed resource constraints, namely, for information reduction and segmentation in image processing, strategies for grasp determination as well as vision-guided control for grasp execution. A preshaping heuristic strategy is combined with symmetry and local shape analysis. Also, the surface of contact between fingers and object is considered and a novel technique for sampling the visuomotor Jacobian is introduced. Experimental validation results are provided showing how the robot arm can efficiently and stably grasp unknown everyday objects.