This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipula...
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This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipulability and joint torque distribution. Due to the competition among various criteria, the multi-criteria optimisation problem typically exhibits many local minima. The emphasis of the paper is put on using genetic algorithms to search for global optimum and solve the minilnax problem for torque distribution. Various simulations for a system including a three-link lnanipulator mounted on a mobile platform show that the proposed genetic algorithm approach performs better than conventional methods.
Many research works have been reported with respect to the relation between neural and fuzzy systems. Looking for a synergistic relation of these technologies, an important property of neural network-based systems is ...
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Many research works have been reported with respect to the relation between neural and fuzzy systems. Looking for a synergistic relation of these technologies, an important property of neural network-based systems is their learning capacity, that permits to embed self-organization in fuzzy logic systems. In this paper, a new neuro-fuzzy system, called FasBack, is proposed, that combines learning based on prediction error minimization and pattern matching. FasBack adds error-based learning to a previously proposed model, called FasArt, which extended and formalized neural networks models of the ART family, as fuzzy logic systems. Experimental results are presented in nonlinear systems identification problems, typically used in the literature.
In order to maintain a high quality service and keep up with the ever increasing demands in the field of public transport, London Transport Buses (LTB) equipped bus routes in a test area in North London with a new sys...
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In order to maintain a high quality service and keep up with the ever increasing demands in the field of public transport, London Transport Buses (LTB) equipped bus routes in a test area in North London with a new system called Countdown. The system calculates arrival time estimations which inform both the customers and the operators of the company about the running of their buses. The calculations should be as accurate as possible in the interest of customer satisfaction. This paper shows that the use of a dynamic Kalman filter instead of a previously used static averaging algorithm, improves the error distribution up to 7% and often reduces maximum absolute error figures significantly. This technique may also prove to be a useful tool for other predicting systems.
A new control system is described for the PUMA 560 industrial robotic manipulator based on transputer networks, where both the hardware and software designs are detailed. A Transputer Interface Board (TIE) establishin...
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A new control system is described for the PUMA 560 industrial robotic manipulator based on transputer networks, where both the hardware and software designs are detailed. A Transputer Interface Board (TIE) establishing a transputer link to the 6503 microprocessors of the PUMA arm joints has been designed, built and tested successfully. In addition to hardware implementation, software testing for this new system had been accomplished. The new system can communicate with the PUMA lower level controller at a much shorter period (i.e. 1.75 ms) than the default 28 ms. Genetic Algorithms are used to plan the PUMA robot minimum-time motion trajectory, which is not possible by the traditional exhaustive search method. Real-time experiments have been carried out based on the new PUMA control platform, and show a very good match with simulations. controlled by the new system, PUMA perfoms better when it is interfaced with the shorter time period. Copyright (C) 1996 Elsevier Science Ltd.
This paper proposes a decentralised compensation scheme for unstructured uncertainties and modelling errors of robotic manipulators. The scheme employs a central decoupler and independent joint neural network compensa...
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This paper proposes a decentralised compensation scheme for unstructured uncertainties and modelling errors of robotic manipulators. The scheme employs a central decoupler and independent joint neural network compensators. Recursive Newton Euler formulas are used to decouple robot dynamics to obtain a set of equations in terms of input and output of each joint. Each joint sub-system is then controlled separately by neural network controllers which suppress the effects of uncertainties associated with the model. Multi-layered perceptrons using back-propagation learning algorithm are employed as the adaptive elements in the control scheme. The effectiveness of the proposed scheme is demonstrated by a simulation experiment on PUMA 560. Simulation results show that this control scheme achieves fast and precise robot motion control under substantial model inaccuracies. Properties of the neural compensation technique are compared with those of a globally stable adaptive controller.
The search for minimum-time motion of an articulated mechanical arm by tessellating the joint space involves heavy computational burden. In this work, Genetic Algorithms (GAs) are used to tackle this problem while con...
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The search for minimum-time motion of an articulated mechanical arm by tessellating the joint space involves heavy computational burden. In this work, Genetic Algorithms (GAs) are used to tackle this problem while considering different optimisation criteria, namely, minimum motion time, constraints on torque commands and constraints on end-point velocities. In addition, the search algorithm considers all constraints imposed on the manipulator design, including bounds on motor torque values. Several case studies are simulated for a two joint planner arm as well as for the PUMA 560 industrial manipulator, with results showing much better performance than those reported in the literature.
This paper is concerned with efficient and fast computations of adaptive control algorithms for robot manipulators. Adaptive controllers employing the highly coupled and non-linear dynamics of manipulators are computa...
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This paper is concerned with efficient and fast computations of adaptive control algorithms for robot manipulators. Adaptive controllers employing the highly coupled and non-linear dynamics of manipulators are computationally complex, which presents a major obstacle in their real-time implementation for industrial applications. A solution to this problem is suggested by utilising a parallel-processing approach. The controller algorithm is implemented for a robot manipulator with six degrees of freedom, utilising a transputer network, for which programming is performed in C. The execution times achieved by the distributed algorithm are well within the limit acceptable for real-time control.
This paper describes a new control system for the PUMA 560 industrial robotic manipulator based on transputer networks, where both the hardware and software designs are detailed. A Transputer Interface Board (ТIB) es...
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This paper describes a new control system for the PUMA 560 industrial robotic manipulator based on transputer networks, where both the hardware and software designs are detailed. A Transputer Interface Board (ТIB) establishing a transputer link to the 6503 microprocessors of the PUMA arm joints has been designed, built and tested. In addition to hardware implementation, software testing for this new system has been successfully accomplished. A great deal of flexibility can be achieved with this new system, yet without many difficulties, where it can be used as a platform to implement advanced control algorithms and develop sensor-based intelligent robotic structures which need much more computational power. Genetic Algorithms are used to plan the PUMA robot motion trajectory based on the new PUMA control platform, and the motion planner runs concurrently with the controller. Real-time experiments show that PUMA moves much more smoothly along the optimum planned trajectory.
A virtual environment was created which mimicked the real-world operation of the LongArm robotic system. In addition to considering an exact model of the physical system, online two-way data exchange between the actua...
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A virtual environment was created which mimicked the real-world operation of the LongArm robotic system. In addition to considering an exact model of the physical system, online two-way data exchange between the actual robot and its virtual counterpart makes actual real-time interaction possible. The designed interface allows the operator to experiment in the virtual mode with any commands later executed on the real system. There is an option to view a second set of graphics showing the kinematic relationships between the joints of the robot. This prototype is intended as an initial investigation into the design of virtual environments for the real-time control of dynamic systems.
The authors present a practical solution to the problem of real-time robot control including the nonlinear dynamic model of the manipulator by employing a parallel processing approach. The parallelism inherent in the ...
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The authors present a practical solution to the problem of real-time robot control including the nonlinear dynamic model of the manipulator by employing a parallel processing approach. The parallelism inherent in the adaptive controllers is exploited to obtain an efficient implementation that reduces the overall computation time to within the limit acceptable for real-time control. The distributed algorithm is implemented on a network of transputers for the six-joint PUMA 560 arm.< >
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