Almost all industrial robot applications in use today are controlled using a control law that is simple and computationally efficient, local joint error feedback. When two or more open chain manipulators cooperate to manipulate the same object - such as in mechanical grippers, walking machines, and cooperating manipulator systems - closed kinematic chain, redundantly actuated mechanisms are formed. Control approaches for this type of system focus on the more computationally intensive computed torque or inverse plant control laws, due to the concern over instability caused by the unspecified distribution of control forces in the redundant actuator space, and due to the constrained motion caused by the closed kinematic chains.

This paper deals with the simulation of a control of an industrial arm using a prediction filter to track an object. It also deals with a developed range-sensing subsystem (range sensor), which, making use of trigonometric principles, can sense the two-dimensional location of the object even if it is moving. First, the hardware and the software of the 2-D range sensor are described. Next, processes to give the arm successive locations to arrive at, using the data obtained by the range sensor are illustrated. Finally some resultant simulation follows.

Traditionally, most industrial robots are programmed by teaching. Automatic planning of robotic tasks has many potential benefits for flexible automation. It allows the user to describe a task to the robot programming system in a formal and natural manner, and reduces the time required to generate and update robot programs. Two main levels of abstraction in describing robot tasks can be identified. Robot-level programming is based on robot movements and actions, as detailed by the programmer. Object-level or task-level programming allows the user to describe assembly tasks in terms of operations performed on objects being manipulated instead of specifying the individual motions of the robot end-effector. However, commercially available robot-level programming languages still fall short of the robot user's need to programme complex tasks and consequently are not widely used in industry. There is an increasing need for integrating sensors feedback into the robot system to provide better perception and for improving the capacity of the robot to reason and make decisions intelligently in real-time. Task-level programming represents the highest level of abstraction and is the most attractive, as it uses reasoning capabilities provided by Artificial Intelligence. To date, no system of this class has been completely implemented in industry. This paper reviews the progress made in robot programming and task planning systems in the last twenty years, and discusses the current research trends.

The Fourier series is employed to approximate the input/output (I/O) characteristics of a dynamic system and, based on the approximation, a new learning control algorithm is proposed in order to find iteratively the control input for tracking a desired trajectory. The use of the Fourier series approximation of I/O renders at least a couple of useful consequences: the frequency characteristics of the system can be used in the controller design and the reconstruction of the system states is not required. The convergence condition of the proposed algorithm is provided and the existence and uniqueness of the desired control input is discussed. The effectiveness of the proposed algorithm is illustrated by computer simulation for a robot trajectory tracking. It is shown that, by adding a feedback term in learning control algorithm, robustness and convergence speed can be improved.

A robot-based automatic system for adjusting energy regulators in electric cookers is described in this paper. It is claimed that this system improves the quality of the regulators and increases productivity. First, the operator's intuitive judgement and decision-making are simulated on a microcomputer; the structure and performance variables of the regulator are then described. A discussion of computer modelling of the regulator then follows, leading to the development of an algorithm for the adjustment procedure and overall strategy of the system. Experiments on 2,000 regulators showed that this automated operation was superior to the manual procedure as regards consistency and accuracy. This technique based on a robot may be applied to quality control and manufacture of a variety of similar products.

One way to increase the flexibility (or versatility) of manipulators is to use links with time varying inertia. One can then control both the joint torques and the inertias of the links therefore more pliancy in the practical utilisation of the arm. The paper deals with the Lagrangian formulation of the corresponding inertia control problem which can be considered either in open loop or in closed loop form. The general equations are derived and it is shown that one so obtains a set of uncoupled Ricatti differential equations which define the dynamics of the structural inertias. Problems related to stabilization and to structural parameter uncertainty are also considered.

A high-level robot programming language constitutes a general purpose interface for accessing the basic functional capabilities of a robot. On the other hand, CAD facilities give the possibility of using a subset of these capabilities in an easier fashion. In this paper, we show how a robot programming language and CAD facilities can be combined to obtain a robot programming system satisfying the need for generality, and allowing an easy connection with the basic robot programming functions. Such a connection is based on a “complete” simulator providing facilities for executing robot control programs on a graphic display, for describing manipulation tasks using interactive graphic tools, for simulating the physical world and its perception through sensors, and for displaying three-dimensional scenes as shaded pictures.