Due to the complexity of teleoperation tasks, human operators figure in the teleoperator perception-decision-control loop. The operator needs an interactive system to handle the huge flow of data between himself and the teleoperator.

The scene represented by the robot and its environment is viewed by one or more cameras. However, the video image may be degraded in extreme environments (underwater, space, etc.) or simply inadequate (2-D image).

In this paper we describe the visual perception aids based of the scene, and more specifically how these are generated by the method we put forward. The system developed at the LRE superimposes a 3-D synthetic image onto the video picture, and animates the scene in real-time on the basis of sensor information feedback. The graphic image can be generated from models, if the objects are known, otherwise interactively, with the cooperation of the operator if the objects are completely unknown. Experiments show that these graphic aids improve the operator's performance in task execution.

The moving acceleration radius (MAR) is proposed as a local performance index quantifying the dynamic uniformity of a redundant robot. MAR can be calculated by a simple sequential algorithm, and the resolution of the redundant joint angles is obtained by maximizing MAR locally. In addition, the reduction of the joint torques is achieved by maximizing the acceleration bound in the direction of work path, while MAR is being kept at a maximum. Also a new differentiation algorithm for angular acceleration is suggested for numerical efficiency as well as accuracy, using a null space operator.

A three degrees of freedom planar robot with one degree of redundancy, simulated using these algorithms for various situations, showed a marked improvement in dynamic characteristics.

The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. In actual practice, however, since conventional manipulators have flexibility at their joints, the improved dynamic properties obtainable for rigid manipulators may be influenced by those joint flexibilities. This paper investigates the effects of the joint flexibility on the dynamic properties and the controlled performance of a balanced robotic manipulator. The natural frequency distribution and damping characteristics were investigated through frequency response analyses. To evaluate the dynamic performance a series of simulation studies of the open loop dynamics were made for various trajectories, operating velocities, and joint stiffnesses. These simulations were also carried out for the balanced manipulator with a PD controller built-in inside motor control loop. The results show that, at low speed, the joint flexibility nearly does not influence the performance of the balanced manipulator, but at high speed it tends to render the balanced manipulator susceptible to vibratory motion and yields large joint deformation error.

A vibratory sensor capable of locating parts by measuring their inertial properties is described in this paper. The sensor is designed to be fitted to the wrist of a robot and used to acquire parts from a stack or a tray. The initial coordinates of the parts need only be approximately known (say to ±25 mm and ±45°). The robot is thus said to operate in a “semi-ordered” environment which can be realised inexpensively as accurate pallets, fixtures or other parts presentation equipment are not required. Furthermore, the absence of complete disorder, as would exist with a “bin-picking” approach where parts are allowed to lie at random in a bin, reduces the degree of sophistication demanded of the sensor system. Consequently, the cost of the latter can also be kept low.

The proposed sensor has a circular platform mounted on the shaft of a motor such that the shaft is normal to the platform. The part whose location is to be determined is held on the platform by magnetic or other means. The assembly consisting of the part, platform and motor is constrained to vibrate about an axis parallel to the platform. Two methods for computing the location of the part relative to the platform are described. Both methods require the natural frequencies of vibration of the part-platform-motor assembly for various angular positions of the platform. A simulation study to examine the effect of various design and operational parameters (inertias of the platform and part, stiffness of the spring restraining the movements of the vibrating assembly, location of part on platform, accuracy of frequency measurements) on the accuracy of the location computed is presented. The simulation results clearly demonstrate the feasibility of the sensor.

The purpose of this paper is to show how one can use entropies of deterministic functions (as previously defined by the author) in order to analyze some questions related to machine vision. The main advantage of this approach is that it provides information theoretic methods for solving problems which basically do not refer to probability distributions. After a short qualitative background on deterministic functions, one applies this theory to edge finding, image segmentation, transfer of information defined by brightness functions, and image processing. Some more theoretical details on the entropies of deterministic functions are given in the appendix.

This paper argues the case for extracting as complete a set of sensory data as practicable from scenes consisting of complex assemblages of objects with the goal of completing the task of scene analysis, including placement, pose, identity and relationship amongst the components in a robust manner which supports goal directed robotic action, including collision-free trajectory planning, grip site location and manipulation of selected object classes.

The emphasis of the paper is that of sensor fusion of range and surface colour data including preliminary results in proximity, surface normal directionality and colour based scene segmentation through semantic-free clustering processes. The larger context is that of imbedding the results of such analysis in a graphics world containing an articulated robotic manipulator and of carrying out experiments in that world prior to replication of safe manipulation sequences in the real world.

This paper describes a revised version of ROBOPLAN, a goal-oriented robot task planning system for automatic generation, decomposition and execution of high-level robot plans for assembly. It emphasizes its new features, i.e., modularity, formal definition of the task, robust plan synthesis, and execution of each assembly step. A task definition language allows a formal description of the robot universe and the assembly task to be input to ROBOPLAN. The expert task planner is a non-linear backward chaining problem solver, using a goal driven depth-first strategy. The implemented search strategy has been tested in the assembly domain, but it could be used in other domains where planning is needed. The motion planner provides a non-optimal, safe robot trajectory; collision free path planning has not been included yet. A robot executable code is generated for each assembly step and monitored in real time. The error detection and recovery capability of the system is rather limited at present, since no sensors are used. The initial implementation of the system has been tested and evaluated on the assembly of a DC motor. The potential of extending this planning framework to other applications is also discussed.

In this paper we present a series of haptic exploratory procedures, or EPs, implemented for a multi-fingered, articulated, sensate robot hand. These EPs are designed to extract specific tactile and kinesthetic information from an object via their purposive invocation by an intelligent robotic system. Taken together, they form an active robotic touch perception system to be used both in extracting information about the environment for internal representation and in acquiring grasps for manipulation. The theory and structure of this robotic haptic system is based upon models of human haptic exploration and information processing.

The haptic system presented utilizes an integrated robotic system consisting of a PUMA 560 robot arm, a JPL/Stanford robot hand, with joint torque sensing in the fingers, a wrist force/torque sensor, and a 256 element, spatially-resolved fingertip tactile array. We describe the EPs implemented for this system and provide experimental results which illustrate how they function and how the information which they extract may be used. In addition to the sensate hand and arm, the robot also contains structured-lighting vision and a Prolog-based reasoning system capable of grasp generation and object categorization. We present a set of simple tasks which show how both grasping and recognition may be enhanced by the addition of active touch perception.

The problem of the constrained motion of robot end-effector is discussed. The redundant robot is considered, redundancy being added in order to improve robot working characteristics. In the phase of free motion towards the constraint the errors of basic non-redundant configuration are corrected by means of redundancy. During the constrained motion redundancy plays the role either of active or passive compliance. Between these two phases, the collision with the constraint occurs, and the impact can be absorbed by using redundancy.

A semi-automatic method for calibrating a robot-vision interface is presented. It puts a small work-load on the operator, requires a simple calibration jig and a solution of a very simple system of equations. It has been extensively used in an experimental robotic cell set up at Loughborough University of Technology, where various aspects of the manufacturing and the decoration of scale models are being investigated. As an extension of the calibration procedure, the paper also shows practical solutions for the problem of dealing with three dimensional objects using a single camera.

The problem of path tracking in robotic manipulators applications is studied in this paper. The path is generated as a sequence of elementary motions. The characteristic feature of our algorithm is that it avoids singularities, because there is no need to use inverse kinematics. Direction and proximity criteria are introduced.

The application of parallel processing methods to path tracking according to the previous algorithm is presented. The algorithm is implemented in the Alliant FX/80 parallel machine.

The purpose of this paper is to describe a system which utilizes an interactive robotic device to help in the educational process of very young disabled children. Within the System the child's performance is monitored and evaluated on line, providing a current prescription for progress to more or less advanced learning levels. The robotic Systems developed at Purdue University during research on this concept are described as well as the prototype Systems to monitor progress of the students

The accurate modelling of robot dynamics is essential for the design of model-based robot controllers. However, dynamic models have very complicated features which can be attributed to several reasons. For example, the continuously-varying arm configuration, uncertain effects of load handling on the dynamic stability of the arm, and the high degree of non-linearity and coupling exhibited between the different links. Hence, the accurate modelling of these effects will play an important role in the design of robust controllers. Towards this end, an efficient and fast method for the on-line tuning of robot dynamic parameters must be devised. This work proposes to solve this problem as follows. First, a simplified dynamic model of the robot is developed. The model allows for direct and straightforward extraction and regrouping of dynamic parameters. The resulting dynamic parameters are formulated as a regression equation which is linear in the dynamic parameters. Finally, the algorithm is executed using a Transputer development system to speed up the computation and meet real-time constraints. The efficiency of the approach is demonstrated by a case study.

This paper presents a practical method for generating task strategies applicable to chamferless and high-precision assembly. The difficulties in devising reliable assembly strategies result from various forms of uncertainty such as imperfect knowledge on the parts being assembled and functional limitations of the assembly devices.

In order to cope with these problems, the robot is provided with the capability of learning the corrective motion in response to the force signal through iterative task execution. The strategy is realized by adopting a learning algorithm and is represented in a binary tree-type database. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out under simulated real production conditions. The experimental results show that sensory signal-to-robot action mapping can be acquired effectively and, consequently, the assembly task can be performed successfully.