This paper discusses the problem of stable grasping and object manipulation by a pair of robot fingers when fingertips are covered with soft compressible material and fingers are allowed to incline their last link against the object surface. The area contact between the fingertips and the rigid object surface leads to nonholonomic constraints even for the planar case; however, the variational principle can be applied and the equation of motion is derived as a set of nonlinear differential equations with extra terms of Langrange multipliers incorporating the constraints. The proposed feedback controller is a linear combination of simple feedback control signals each designed for realizing grasp stabilization, regulation of object rotation and regulation of object position respectively. The controller is shown to achieve asymptotic convergence to the desired state at a stable grasping configuration. Simulation results are presented confirming the theoretical findings.

In this paper we propose an analytical formulation for simulation and design of a one d.o.f. articulated finger mechanism with three phalanges. The formulation is based on a study of the design and operation of an index human finger. In particular, we have proposed a suitable mechanical design for an anthropomorphic finger as both an approximation of human architecture and an easy practical design. Kinematic characteristics are illustrated with numerical examples.

This paper presents a neural model to solve the visual-tactile-motor coordination problem in robotic applications. The proposed neural controller is based on the VAMC (Vector Associative Map) model. This algorithm is based on the human biological system and has the ability of learning the mapping that establishes the relationship between the spatial and the motor coordinates. These spatial inputs are composed of visual and force parameters. The LINCE stereohead carries out a visual detection process, detecting the positions of the object and of the manipulator. The artificial tactile skins placed over the two fingers of the gripper measure the force distribution when an object is touched. The neural controller has been implemented for robotic operations of reaching and object grasping. The reaching process is fed back in order to minimize the Difference Vector (DV) between the visual projections of the object and the manipulator. The stable grasping task processes the force distribution maps detected in the contact with the two surfaces of the gripper, in order to direct the object into the robotic fingers. Experimental results have demonstrated the robustness of the model and the accuracy of the final pick-and-place process.

This paper rechecks the relative degree of the end-point tracking control system of a flexible manipulator. New added insights into the ill-defined behavior of the relative degree are presented by constructing a perturbed truncation model. The implications for the inverse dynamics motivate us to reformulate the inverse dynamics based on the perturbed truncation model in the extreme case of truncating all of the flexible modes. New potential advantages arising from this novel formulation are investigated for the inverse dynamics control design as well.

This paper addresses the problem of finding a nonholonomic path subject to a curvature restriction, to be tracked by a wheeled autonomous navigation vehicle. This robot is able to navigate in a structured environment, with obstacles modeled as polygons, thus constituting a model based system. The path planning methodology begins with the conditioning of the polygonal environment by offsetting each polygon in order to avoid the possibility of collision with the mobile. Next, the modified polygonal environment is used to compute a preliminary shortest path (PA) between the two extreme positions of the trajectory in the plane (x, y). This preliminary path (PA) does not yet consider the restrictions on the curvature and is formed only by straight line segments. A smoothing process follows in order to obtain a path (PS) that satisfies curvature restrictions which consist basically of joining the straight line segments by circular arcs of minimum radius R (filleting). Finally, the initial and final orientation of the vehicle are accounted for. This is done using a technique we have called the Star Algorithm, because of the geometric shape of the resulting maneuvers. A final complete path (PC) is thus obtained.

In this paper, two algorithms for solving the Inverse Dynamic Problem based on the Gibbs-Appell equations are proposed and verified. Both are developed using mainly vectorial variables, and the equations are expressed in a recursive form. The first algorithm has a computational complexity of O(n2) and is the least efficient of the two; the second algorithm has a computational complexity of O(n). This algorithm will be compared with one based on Newton-Euler equations of motion, formulated in a similar way, and using mainly vectors in its recursive formulation. The O(n) proposed algorithm will be used to solve the Inverse Dynamic Problem in a PUMA industrial robot.

Since a slave manipulator with a high reduction ratio joint generally has slow dynamics in comparison with a master manipulator in telemanipulation systems, its control input is likely to be saturated resulting in poor position tracking performance and deteriorated stability. This paper proposes a force reflecting control scheme for telemanipulators with a high reduction ratio joint, which can effectively compensate the control input saturation caused by the high ratio gear reducer at its joint. The proposed scheme is also shown to guarantee the stability and provides an excellent position tracking performance regardless of the saturation.

This paper concerns the issue of mechanism design of a simplified 6-DOF 6-RUS parallel manipulator. The design of robotic mechanisms, especially for 6-DOF parallel manipulators, is an important and challenging problem in the field of robotics. This paper presents a design method for robotic mechanisms, which is based on the physical model of the solution space. The physical model of the solution space, which can transfer a multi-dimensional problem to a two or three-dimensional one, is a useful tool to obtain all kinds of performance atlases. In this paper, the physical model of the solution space for spatial 6-RUS (R stands for revolute joint, U universal joint and S spherical joint) parallel manipulators is established. The atlases of performances, such as workspace and global conditioning index, are plotted in the physical model of the solution space. The atlases are useful for the mechanism design of the 6-RUS parallel manipulators. The technique used in this paper can be applied to the design of other robots.

This paper presents an investigation into the development of parametric and non-parametric approaches for dynamic modelling of a flexible manipulator system. The least mean squares, recursive least squares and genetic algorithms are used to obtain linear parametric models of the system. Moreover, non-parametric models of the system are developed using a non-linear AutoRegressive process with eXogeneous input model structure with multi-layered perceptron and radial basis function neural networks. The system is in each case modelled from the input torque to hub-angle, hub-velocity and end-point acceleration outputs. The models are validated using several validation tests. Finally, a comparative assessment of the approaches used is presented and discussed in terms of accuracy, efficiency and estimation of the vibration modes of the system.