Grasping and object manipulation is a key element of intelligent behavior. Many innovative cyberphysical systems involve some kind of object grasp and manipulation, to the extent that grasping has been recognized as a critical technology for the next generation industrial systems. In this survey, we aim to draw a broad landscape of applications and current research trends and topics relating to grasping techniques and tools. Applications range from biomedical and surgical to industrial warehouse pick and place tasks, covering a wide range of spatial scales, from micro to macro scales. The resources involved and research lines under development include the latest computational intelligence tools as well as the research on new materials and devices for sensing and actuation.

Intelligent environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change of perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behavior to the users, even anticipating their needs, preferences, or habits. For this reason, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behavior becomes an essential aspect for the successful implementation of IEs. This paper presents a system, learning frequent patterns of user behavior system (LFPUBS), that discovers users' frequent behaviors taking into consideration the specific features of IEs. The core of LFPUBS is the learning layer, which, unlike some other components, is independent of the particular environment in which the system is being applied. On one hand, it includes a language that allows the representation of discovered behaviors in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviors has been designed and implemented. For this reason, it uses association, workflow mining, clustering, and classification techniques. LFPUBS was validated using data collected from two real environments. In MavPad environment, LFPUBS was tested with different confidence levels using data collected in three different trials, whereas in a WSU Smart Apartment environment LFPUBS was able to discover a predefined behavior.

Intelligent Environments depend on their capability to understand and anticipate user's habits and needs. Therefore, learning user's common behaviours becomes an important step towards allowing an environment to provide such personalized services. Due to the complexity of the entire learning system, this paper will focus on the automatic discovering of models of user's behaviours. Discovering the models means to discover the order of such actions, representing user's behaviours as sequences of actions.

Intelligent Environments depend on their capability to understand and anticipate user's habits and needs. Therefore, learning user's common behaviours becomes an important step towards allowing an environment to provide such personalized services. We have developed a system which learns user's patterns taking into account the special features of Intelligent Environments. Due to the complexity of the system, this paper will focus on a specific part of such a system, the module that discovers what sets of actions are frequently carried out by the user.

The decentralized control of robot manipulators at low speed needs the calculation of the inertial and gravitational coefficients of the dynamic equations. The scheme of calculation of the generalized forces can then be divided into two parts: the calculation in background of the dynamic coefficients and the calculation in real time of the generalized forces [1]. A LISP program, due to its ability for symbolic manipulation, has been created for the automatic generation of these coefficients. The input to the program is a file containing the Denavit-Hartemberg parameters, masses, centers of gravity and moments of inertia of the links. The result is a C program that calculates the coefficients. The validity of the program has been demonstrated by numerical comparison with the Newton-Euler program. Two examples, the PUMA robot and the Stanford manipulator have been tested. The former needs only 270 multiplications and 220 additions for the manipulator with an arbitrary load, and 200 multiplications and 150 additions without load; the latter needs 160 multiplications and 120 additions with an arbitrary load and 120 multiplications and 100 additions without load. The total time of calculation of the coefficients in a 8086/8087 microprocessor needs only 22 milliseconds in background.

We present a method for the computation of the effective inertia, coupling inertia, and gravity loading terms for a manipulator with a load. The calculations are separated so as to consider the manipulator and the load independently. These inertial and gravity loading effects are additive and furthermore their computation need only be performed at a rate commensurate with changes in the configuration of the manipulator, not at the control sample rate. As an example we compute the terms for the PUMA manipulator. Taking advantage of the symmetries in the manipulator we need only 65 multiplications and 41 additions to compute all terms. The computations for a load requires an additional 160 multiplications and 101 additions if the load is considered as a point mass.

A new method of calibration is proposed for active visual sensing for use in 3D analysis. The method, based on an adaptation of the two camera plane m model, permits the calibration of a mobile camera as a function of the position and orientation of the camera.