Part II continues to develop a hyper-stability framework for presenting physical interpretations of dexterous motion controls, such as hybrid position/force, impedance, model-based adaptive, learning control and coordination. In all cases passivity induced by introduction of a quasi-natural potential plays a key role. In view of these considerations, the final section discusses the possibility of development of a nonlinear circuit theory based on feedback connections of hyper-stable blocks, which may give rise to a physical understanding of dexterous and skilled motions for nonlinear mechanical systems and, eventually lead to the design of intelligent functions implementable in robotic machines.

Identification of inertia constants and joint frictions of a robot manipulator is achieved in situ, without dismantling operations, by means of specific test motions. The necessary estimation of actuating torques is carried out by measuring, with Hall effect transducers, the current absorbed by the motors which power the system. This identification is accomplished by using a precise methodological order adapted to a planar SCARA type manipulator with two degrees of freedom. The identification of friction laws underscores a hysteresis phenomenon of the dissipative torques. This indicates that friction doesn't result from a simple superposition of a dry friction law and a viscous damping law. The identification results were applied with success to implementation of optimized trajectories computed on the basis of a dynamic criterion. The effective minimization of the performance criterion along the optimized trajectories, according to the corresponding standard trajectories, was verified experimentally by evaluating the motor work and actuator torques.

This paper studies the static rigidity behaviour of a parallel manipulator with legs modelled as elastic members under axial loading. Structurally, a parallel module is more rigid compared to a serial module and is expected to take heavier payloads. Therefore, a guidance for design of such parallel manipulators is needed which leads to maximum rigidity over the workspace. In the present work, the authors propose the concept of the flexibility ellipsoid for a parallel system. Various scalar measures of rigidity are formulated on the basis of the proposed ellipsoid. An algorithm, involving multiple objective nonlinear programming technique, is implemented to decide upon some important design parameters of a generalised six degrees of freedom Stewart platform type parallel manipulator. It is observed that irrespective of the other parameters, parallel manipulators with the legs pairwise joined at the top platform possess the highest rigidity. Moreover, there exists certain kinematic dimensions for which the designed parallel system is completely free from all sorts of singularity.

In this paper we propose some simple rules for PID tuning of robot manipulators. The procedure suggested requires the knowledge of the structure of the inertia matrix and the gravitational torque vector of the robot dynamics, but only upper bounds on the dynamics parameters are needed. This tuning procedure is extracted from the stability analysis by using a suitable Lyapunov function together with the LaSalle invariance principle. We show that with this guideline, the overall closed-loop system is asymptotically stable. This procedure is illustrated for a two degrees-of-freedom robot

An algorithm for the motion planning of the multifingered hand is proposed to generate finite displacements and changes in orientation of objects by considering sliding contacts as well as rolling contacts between the fingertip and the object at the contact point. Specifically, a nonlinear optimization problem is firstly formulated and solved to find the minimum joint velocity and the minimum contact force to impart a desired motion to the object at each time step. Then, the relative velocity at the contact point is found by calculating the velocity of the fingertip and the object at the contact point. Finally, time derivatives of the surface variables and the contact angle of the fingertip and the object at the current time step is computed using the Montana's contact equation to find the contact parameters of the fingertip and the object at the next time step. To show the validity of the proposed algorithm, a numerical example is illustrated by employing the robotic hand manipulating a sphere with three fingers each of which has four joints

A new mathematical formulation of robot and obstacles is presented such that for on-line collision recognition only robot joint positions in the workspace are required. This reduces calculation time essentially because joint positions in workspace can be computed every time from the joint variables through robot geometry. It is assumed that the obstacles in the workspace of the manipulator are represented by convex polygons. For every link of the redundant robot and every obstacle a boundary ellipse is defined in workspace such that there is no collision if the robot joints are outside this ellipsis.

In addition to this, a collision avoidance method is presented which allows the use of redundant degrees of freedom such that a manipulator can avoid obstacles while tracking the desired end-effector trajectory. The method is based on the generalized inverse with boundary ellipse functions as optimization criteria. The method permits the tip of the hand to approach any arbitrary point in the free space while the kinematic control algorithm maximizes the boundary ellipse function of the critical link. The effectiveness of the proposed methods is discussed by theoretical considerations and illustrated by simulations of the motion of three- and four-link planar manipulators between obstacles.

An algorithm is presented for the recognition of a set of prototype, solid objects. Cost estimate and simulation results suggest that the algorithm would provide rapid recognition of the objects over a greater range of horizontal, vertical and rotational errors, in the position of the object than is likely to be encountered in practice.

This paper presents a method to obtain near minimum-time trajectories for two coordinated manipulators handling a rigid object. A piece-wise constant function is used to approximate the second derivatives of the generalised coordinates of the manipulators of the system. This transforms the time optimal control problem into a non-linearly constrained optimisation problem. The transformed problem is then solved by the sequential quadratic programming technique. A numerical example involving two SCARA type manipulators handling a long beam is used to illustrate the proposed scheme.

Despite its known effectiveness, a typical vibratory assembly method tends to generate adverse impact forces between mating parts commensurate with the relatively large vibratory motion required for reliably compensating positioning errors of arbitrary magnitude. To this end, this paper presents a neural network-based vibratory assembly method with its emphasis on reducing the mating forces for chamferless prismatic parts. In this method, the interactive force is effectively suppressed by reducing the amplitude of vibratory motion, while the greater part of the relative positioning error is estimated and compensated by a neural network. The estimation performance of the neural network and the overall performance of the assembly method are evaluated experimentally. Experimental results show that the assembly is efficiently accomplished with small reaction forces, and the possible insertion error range is also expanded

We describe a sensor integration system which we have designed and implemented for a large fully-integrated flexible assembly machine. The machine was developed by a consortium to explore the design and implementation issues involved. The sensor integration system was designed for execution on a multiple transputer architecture, and co-ordinates all the sensory information in the machine. It uses the concept of virtual sensing to provide sensory data at an appropriate level of abstraction to the machine supervisor, which controls execution of the assembly tasks.

A light-weight, high-torque actuator with accurate torque control capability is described. The actuator uses a small hydrostatic transmission to achieve the advantage of large gear reduction from a high speed DC motor, and retains accurate joint torque sensing and control capabilities with no backlash. A disadvantage of the actuator is that is introduces extra dynamics which must be accounted for in robot control systems. It is shown that state feedback enables closed loop control of joint torque, with full back drivability, through an effective gear ratio of 485:1 for the experimental system. The actuator can therefore be used for both position control and output force control, which is essential for modern robot control algorithms. A mathematical model of the system is presented in this paper along with experimental results.

In the tracking control of manipulators via the sliding scheme, it may happen that sometimes, because of various inaccuracies, the definition of the actual sliding surface involves errors terms which may be either deterministic or, on the contrary, stochastic. This paper considers this last case and shows how one can estimate the new performances of the system so disturbed. A stochastic Hamilton's principle is applied, by combining the Lagrange parameter technique with results of the dynamic programming approach.