This paper deals with the motivation behind robotics. It surveys the different goals of robotics research and analyses the ways in which robotics could help in various domains. After discussing the task specifications, emphasis is placed on three research sub-cultures that form separate robot “worlds”, viz. the Model World, the Industrial World and the Human World. The paper concludes with a case study to illustrate the need to reduce rash predictions and stimulate better science.

This paper deals with the control technique of a computer-controlled manipulator with high nonlinearity. To overcome the nonlinearity, a linearization of the system by nonlinear feedback has been employed. Because of the difficulty of the parameter identification under the variation of load, it is not easy to make correct nonlinear compensation for its linearization. In this paper, to solve this problem a robust servo controller based on a model is designed for the linearized manipulator, and a control system is constructed taking account of input nonlinearity. The method is applied to the three-joint manipulator endowed with a software servo using a minicomputer, and the effect of the proposed method is investigated.

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.

There is a great deal to be done in machine tool and robot programming research. The major problems include the slowness of the introduction of machine controllers, which have similar operating system and interfacing capabilities as the current 16 bit and 32 bit microcomputers, and the lack of intelligent, high-level standard languages, providing access not only at a high level, but also at the robot system programming level. The introduced and illustrated “MARTI” off-line robot program generator, under development by the author, attempts to provide software in this area.

This paper outlines a heuristic approach for the development of a control strategy for finding a safe path for a polyhedral object moving amongst known polyhedral objects. This is achieved by the algorithm A* for finding a minimum cost path in a graph; the algorithm is implemented on a computer under the UNIX operating system, using the NU7 version of PROLOG.

The production system has been used extensively in Artificial Intelligence systems due, possibly, to a lack of a prior procedural orientation towards the knowledge embedded within it. As a result, this paradigm is especially useful in modeling domains in which a strong procedural correlation of data would not naturally appear. The production system is therefore an appropriate tool for designing models of systems of independent processes whose interactions can be defined in terms of system state.

We are interested in modeling continuous, concurrent processes for simulating robot activities, and present a description of a system which is capable of representing such processes as symbolic expressions within a production system database. This system implements a world model which acts as a continuous function of time, and a set of rules written in a language capable of specifying time-dependent properties of the model. In addition, rules may specify new rules, thus permitting processes to be mapped out over time as sequences of events.

The methodology presented in this paper is an attempt to utilize the power of symbolic programming in a design for world modeling, and a characterization of the requirements for applying such systems to problem domains which contain a continuous parameter, such as time. We believe that the technique of embedding time used here is appropriate for expressing the dynamic evolution of these models.