This paper presents a framework for the design of a hierarchical Simulator of a robotized sequential technological process. The framework employs concepts of discrerte event simulation modelling. The Simulator consists of two layers: the Simulator of a robot and technological process, and the interpreter and planner of robot tasks. A format specification of both layers is presented. The proposed simulation approach is expected to result in significant improvements in the robot task plan generation and in higher efficiency of a technological process.

A general method is presented to automatically determine the placement of manipulators which allows one to optimize multiple kinematic performances indices during the execution of their tasks. It considers the presence of obstacles in the workstation and constraints on the motion of the manipulator's joints. The complete formulation is included, and an example with a six-degree-of-freedom manipulator in a cluttered environment es solved that demonstrates the improvements achieved for the manipulator performances by applying the method.

The application of the joint force sensory feedback in both the gross and fine motion control of manipulation robots is considered in the paper. One of the objectives of the paper is to give a historical overview how the idea of the joint force sensory feedback has appeared and developed in the past two decades. The control schemes, which include joint torque sensory feedback, are surveyed in the paper. The main advantages of this approach are discussed: the joint torque feedback offers an elegant way to compensate for the effects of the robot dynamics without real time computation of the robot dynamics, the control schemes are robust in respect of parameter variations. Various problems regarding design and implementation of joint torque sensor are also considered. Special emphasis is given to the application of this approach in hybrid position/force control schemes.

This paper presents a new approach for simplifying dynamic equations of motion of robot manipulators by using a nondimensionalization scheme. With this approach the dynamic analysis is done in a nondimensional space. That is, it is required to establish a dimensionless coordinate system in which the dynamic equations of motion of manipulators are formulated. The characteristic parameters of the manipulators are then defined by choosing proper physical quantities as basic units for nondimensionalization. Within the nondimensional space the Lagrange method is applied to the manipulator to obtain a set of general dimensionless equations of motion. This dimensionless dynamic formulation of manipulators leads to an easier way to simplify the dynamic formulation by neglecting insignificant terms using the order of magnitude comparison. The dimensionless dynamic model and its simplified version of PUMA 560 robot are implemented using the proposed approach. It is found that the simplified dynamic model greatly reduces the computation burden of the inverse dynamics. Simulation results also show that the simplified model is extremely accurate. This implies that the proposed nondimensional simplification emethod is reliable.

Learning control is a new approach to the probelm of skill refinement for robotic manipulators. It is considered to be a mathematical model of motor program learning for skilled motions in the central nervous system.

This paper proposes a class of learning control algorithms for improving operations of the robot arm under a geometrical end-point constraint at the next trial on the basis of the previous operation data. The command input torque is updated by a linear modification of present joint velocity errors deviated from the desired velocity trajectory in addition to the previous input. It is shown that motion trajectories approach an e-neighborhood of the desired one in the sense of squared integral norm provided the local feedback loop consists of both position and velocity feedbacks plus a feedback term of the error force vector between the reactive force and desired force on the end-point constrained surface. It is explored that various passivity properties of residual error dynamics of the manipulator play a crucial role in the proof of uniform boundedness and convergence of position and velocity trajectories.