A learning control algorithm is proposed for hybrid force/position tracking of a manipulator in contact with a hard surface. The algorithm utilizes acceleration errors and force errors for learning the inputs required to perform repetitive tasks. We prove the convergence of both force and position tracking errors theoretically. The speed of convergence of the tracking errors is shown to be improved by proper choice of learning gains. The method is compared with PI and PID learning, and similarities with the free-space case are outlined. The robustness of the new scheme to uncertainties in surface geometry and link parameters is discussed. Results of simulation studies and experimental implementation on 3-link robot manipulators are presented to illustrate the effectiveness of the proposed learning controller.

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.

A prototype robot system for automated handling of flexible electrical wires of variable length is described. The handling process involves the selection of a single wire from a tray of many, grasping the wire close to its end with a robot manipulator, and either placing the end in a crimping press or, for tinning applications, dipping the end in a bath of molten solder. This system relies exclusively on the use of vision to identify the position and orientation of the wires prior to their being grasped by the robot end-effector. Two distinct vision algorithms are presented. The first approach utilises binary imaging techniques and involves object segmentation by thresholding followed by thinning and image analysis. An alternative general-purpose approach, based on more robust grey-scale processing techniques, is also described. This approach relies in the analysis of object boundaries generated using a dynamic contour-following algorithm. A simple Robot Control Language (RCL) is described which facilitates robot control in a Cartesian frame of reference and object description using frames (homogeneous transformations). The integration of this language with the robot vision system is detailed, and, in particular, a camera model which compensates for both photometric distortion and manipulator inaccuracies is presented. The system has been implemented using conventional computer architectures; average sensing cycle times of two and six seconds have been achieved for the grey-scale and binary vision algorithms, respectively.

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.

The work demonstrates a new approach to design of a level of intelligent control of robotic systems. The analytical model results in operational decisions. The structure of these decisions make them readily available to be implemented as an expert system. The approach is applied to a case study of mobile supervisory robots. The model presented here was motivated by manufacturing robotic systems and a type of autonomous robots that collect information at different sites for safety and other control purposes. The robot actions are directly affected by the obta~ned data. At each site the amount of available information (and thus the correctness of the robot decision) can be increased if the robot keeps collecting data at that site for a longer period of t~me. This means a delay in reacting and in reaching next site and accordingly, a decrease in the general amount of robot's information on the whole system.

The method of finding an economic amount of information collected by a robot at each site is based on the theory of controlled discrete event stochastic systems developed in our earlier works. This theory combines he basic concepts of discrete event control extended to stochastic systems with some aspects of information economics.

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.

Off-line programming of robots has a number of clear advantages over traditional ‘teach1’ methods which require the robot to be taken out of production. However, off-line programming techniques require extensive geometric facilities which are ideally provided by a geometric modelling system.

The use of modelling covers the planning of the robot workcell, as well as the detailed planning of the robot operations. Assembly, in particular, requires detailed geometric information concerning geometric features and solid properties.

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.

An algorithm is presented for the on-line generation of minimum-time trajectories for robot manipulators. The algorithm is designed for intelligent robots with advanced on-board sensory equipment which can provide the position and orientation of the end-effector. Planning is performed in the configuration (joint) space by the use of optimised combined polynomial splines, along with a search technique to identify the best minimum-time trajectory. The method proposed considers all physical and dynamical limitations inherent in the manipulator design, in addition to any geometric path constraints. Meeting the demands of the heavy computations involved lead to a distributed formulation on a multiprocessor system, for which an intelligent control unit has been created to supervise its proper and practical implementation. Simulation results of a proposed case study are presented for a PUMA 560 robot manipulator.

Engineers have traditionally invested in Advanced Manufacturing Technology (AMT), such as FMS, Robots, CAD/CAM, CNC and MRP to achieve a reduction in the labour force. Similarly, government incentives for introducing AMT, have also placed an undue emphasis on the ‘need to replace labour with technology’. This policy creates severe industrial relations problems and leads to the view that technology, especially robots, ‘competes’ with human beings for employment. Research at U.M.I.S.T. has shown that AMT is much more viable when the objective of the investment is to increase a company's overall competitive ability, thereby generating increased sales which, in turn, necessitates an INCREASED labour force. The paper discusses various aspects of AMT and in each case it is shown how the acceptability of the investment is improved when the new technology is viewed ‘company-wide’. When this occurs, technology is viewed as being ‘complementary to’ and not ‘instead of’ humans.