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 research results described present the performance of the Generalized Predictive Control (GPC) algorithm with a changing estimator and predictor model order for a specific application. The application is a hydraulically actuated heavy duty manipulator. Hydraulically actuated robotic manipulators, used in the large resource based industries, have a complex dynamic response in which, primarily due to the hydraulic actuator subsystems, the order of the dynamic model is not initially known and can change as the manipulator is operated. A nonlinear simulation model of the manipulator system is utilized in the work and the GPC controller is implemented with a CARIMA estimator together with an on-line, gradient based estimator model order determination technique. The results given show that with proper use of the order determination technique cost function and tuning of the GPC parameters, good performance and stability can be achieved.

The kinematic structure of closed chain mechanisms is suitable for putting an excess number of inputs in addition to the required number to control their position and orientation. With the addition of these excess inputs, it is possible to generate and control the internal forces within the mechanisms by simultaneously activating joints whose action opposes each other. The internal forces balance inside the structure of mechanisms and do not create any resultant force. However, at the static or quasi-static cases they change the stiffness of the mechanisms. In dynamic cases, in addition to the change in stiffness, they may also generate a damping effect. As a result of these effects, the dynamic behavior of closed chain mechanisms changes. This study analyzes the dynamics of the closed chain mechanisms under the effect of internal forces through the simulation and experimental results using a planar closed chain mechanism.

For a robotic workcell with multiple robots, several interconnection methods are presented in terms of a processor-based architecture. The concept of the multiple processor system (MPS) or multiple computer system (MCS) is used to formulate and analyze the multi-robot interconnection system (MRIS). The MRIS is modelled as a queueing network, and mathematical analysis is done on the basis of modelling. Performance evaluation is achieved for the MRIS through the mean value analysis with the response time and the probability of service failure under different workloads. The results together with some comments suggest a useful guideline for a selecting an appropriate interconnection method for the MRIS subject to the system environment and application.

Topics relevant to modeling and control of mobile wheeled robots with a differential drive are discussed by assuming a motion that is planar and free from lateral and longitudinal slippages, and by taking into account dynamic and kinematic properties of the vehicle. Based on the concept of geometric path-tracking, a controller is designed that is a memoryless function of the lateral, heading, and velocity path-tracking offsets. If these offsets are kept small and the assigned tracking velocity is constant, then this controller may be given a linear, time-invariant and decoupled PID (Proportional + integral + derivative) structure.

For redundant robot kinematics with a degree of redundancy 1 a self-motion vector field is examined whose equilibrium points lie at singular configurations of the kinematics, and whose orbits determine the self-motion manifolds. It is proved that the self-motion vector field is divergence-free. Locally, around singular configurations of corank 1, the self-motion vector field defines a 2-dimensional Hamiltonian dynamical system. An analysis of the phase portrait of this system in a neighbourhood of a singular configuration solves completely the question of avoidability or unavoidability of this configuration. Complementarily, sufficient conditions for avoidability and unavoidability are proposed in an analytic form involving the self-motion Hamilton function. The approach is illustrated with examples. A connection with normal forms of kinematics is established.

In this paper a method of minimal neighborhood for cost optimal trajectory planning along prescribed paths is introduced. The method exploits the phase-plane approach. In the phase-plane, in an iterative procedure, subareas of search are built, called neighborings, which surround the current-best trajectory. In each iteration, in order to find the next-best trajectory, the dynamic programming (pruned to the subarea) is used. The method of minimal neighborhood makes the neighborings as small as possible and therefore speeds up computations maximally. The tests carried out on a model of the IRb-6 ASEA robot have shown that the method of minimal neighborhood is much faster than dynamic programming applied to the whole phase-plane, while preserving the quality of the resulting trajectory.

This paper presents a method to obtain near minimum-time trajectories for two coordinated manipulators handling a rigid object. A piece-wise constant function is used to approximate the second derivatives of the generalised coordinates of the manipulators of the system. This transforms the time optimal control problem into a non-linearly constrained optimisation problem. The transformed problem is then solved by the sequential quadratic programming technique. A numerical example involving two SCARA type manipulators handling a long beam is used to illustrate the proposed scheme.

This paper considers the use of the Taguchi method as a means to identify the optimum design of a robot sensor. The sensor is modelled by a complex set of equations which makes optimisation using traditional calculusbased methods difficult. The Taguchi method is employed as a systematic method to understand the performance of the sensor whilst using a limited number of model evaluations. The Taguchi method is based on the experimental design technique and the results of using a full factorial design, 1/2 and 1/4 fractional factorial designs are compared. The advantage of using the Taguchi method over traditional experimental techniques is also discussed.

Despite its known effectiveness, a typical vibratory assembly method tends to generate adverse impact forces between mating parts commensurate with the relatively large vibratory motion required for reliably compensating positioning errors of arbitrary magnitude. To this end, this paper presents a neural network-based vibratory assembly method with its emphasis on reducing the mating forces for chamferless prismatic parts. In this method, the interactive force is effectively suppressed by reducing the amplitude of vibratory motion, while the greater part of the relative positioning error is estimated and compensated by a neural network. The estimation performance of the neural network and the overall performance of the assembly method are evaluated experimentally. Experimental results show that the assembly is efficiently accomplished with small reaction forces, and the possible insertion error range is also expanded

A major problem in inverse kinematics algorithms is that the generated joint velocities to be fed to the joint servos may cause violation of the speed limits of the joint actuators. In this paper, it is shown how to properly cope with joint velocity limits in first-order inverse kinematics algorithms; the proposed technique guarantees tracking of the desired end-effector path. This goal is achieved by suitably slowing down the task-space trajectory when joint velocity limits are encountered. The time law is modified through a time warp such that the introduced virtual time allows fulfillment of the velocity constraints. A case study is developed to show the effectiveness of the proposed method and a kinematic control scheme based on the presented technique is implemented to demonstrate feasibility under real-time constraints.

This paper is focused on task scheduling in multiprocessor robot controllers. To minimize the input-output time delay our consideration is restricted to parallel architectures that include complete crossbar interconnection networks. In this paper, an efficient scheduling algorithm based on a heuristic function is considered. This function takes into account delays caused by interprocessor communication and minimizes both the execution time and the communication cost. Robot control computation based on a highly efficient customized symbolic method is decomposed into a large number of simple tasks, each involving a single floating-point operation. Starting with an empty partial schedule, each step of the search extends the current partial schedule by adding one of the tasks yet to be scheduled. The heuristic function used in the algorithm actively directs the search for a feasible schedule, i.e. it helps choose the task that extends the current partial schedule. To increase the computational rate we introduced overlapping of computations.

An input shaping technique using a time-varying impulse sequence is presented to reduce the motion-induced vibration of flexible systems in a feedforward way.

The decoupled modal responses for a general linear time-varying system are firstly approximated. Upon this approximation, the time-varying impulse sequences to suppress the vibrational modes are found. The reference inputs to the systems are shaped by convolving with the time-varying impulse sequence to suppress the multimode vibrations. This technique can be also applied to suppress the vibration of nonlinear time-varying systems.

The performance of this method is demonstrated with two practical examples: a moving overhead crane and a two-link robot manipulator. Consequently, this study provides an input shaping technique applicable to the vibration suppression of broader classes of flexible systems.

This paper presents research work which crosses the divide between “Path Planning” and “Trajectory Generation & Tracking”. These subjects have tended to develop along two parallel lines, in isolation from each other. The work uses the Lagrange formulation for the three major joints of an industrial manipulator. Simple planning rules are developed from this formulation. The rules are used to select new paths for a manipulator rather than to improve preplanned trajectories. The method has been applied to pick and place tasks requiring gross motions of the manipulator and the results are presented. It is shown that using this method, pre-planned manipulator paths can be modified to improve the gross motions associated with the pick and place task.

We describe a sensor integration system which we have designed and implemented for a large fully-integrated flexible assembly machine. The machine was developed by a consortium to explore the design and implementation issues involved. The sensor integration system was designed for execution on a multiple transputer architecture, and co-ordinates all the sensory information in the machine. It uses the concept of virtual sensing to provide sensory data at an appropriate level of abstraction to the machine supervisor, which controls execution of the assembly tasks.

The paper addresses the following problem: for a three-bar lifting linkage, find the locations of the two ends of the actuated link so as to minimize the maximum value of the force seen by the actuator over a specified range of arm angular positions. A simple analytical solution to this min-max problem is given, showing that the optimal locations are with the actuated link perpendicular either to the fixed link or to the lifting arm, when the arm is in a horizontal position. The method can be used to optimize the actuation of robots, doors, platforms, landing gears, etc., and allows to account for actuator length constraints. It can be extended also to Stewart's platform-type spatial manipulators.

A novel design of a three dimensional localiser intended for autonomous robot vehicles is presented. A prototype is implemented in air using ultrasonic beacons at known positions, and can be adapted to underwater environments where it has important applications, such as deep sea maintenance, data collection and reconnaissance tasks. The paper presents the hardware design, algorithms for position and orientation determination (six degrees of freedom), and performance results of a laboratory prototype. Two approaches are discussed for position and orientation determination – (i) fast single measurement set techniques and (ii) computationally slower Kalman filter based techniques. The Kalman filter approach allows the incorporation of robot motion information, more accurate beacon modelling and the capability of processing data from more than four beacons, the minimum number required for localisation.

This paper discusses a novel design for a rotary differential NiTi shape memory alloy actuator, which incorporates a mobile heat sink. The new mechanism is described in detail, and its performance in a position control system is compared with that of the same actuator without heat sink. Experimental results are presented which show an improvement in actuator response time, as well as closed-loop bandwidth. The design of the mechanism gives enhanced performance, without the increased power consumption associated with fixed heat sinks.

This paper details the design principles of operation of a pneumatic proximity-to-tactile sensing device for part handling and recognition in a flexible manufacturing environment. The sensing device utilises a densely packed line array of piezoresistive pressure sensors, providing continuous variable outputs. The sensing plane of the device incorporates a corresponding line array of air jets which develop an air cushion when striking a target of interest. The back pressure levels from these air jets form the basis for the task of target detection and recognition.