Chung, W-Y & Waldron, KJ 1995, 'An Integrated Control Strategy for Multifingered Systems', Journal of Dynamic Systems, Measurement, and Control, vol. 117, no. 1, pp. 37-42.
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A method of force allocation by optimizing the friction angles at finger contacts was combined with the computed torque method to find the torques to be commanded at finger joints for multifingered systems. In this way, slip can be avoided when the object is grasped or manipulated. The proposed method can be used to efficiently find the input torques, and is applicable for real-time computation. A history-based method is also proposed to improve the smoothness of the input torque commands. Three-dimensional simulation results are given.
Nanua, P & Waldron, KJ 1995, 'Energy Comparison Between Trot, Bound, and Gallop Using a Simple Model', Journal of Biomechanical Engineering, vol. 117, no. 4, pp. 466-473.
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In this paper, the dynamics of quadruped trot, gallop, and bound will be examined using a simple model for the quadruped. The body of the quadruped is modeled as a uniform bar and the legs are modeled by massless springs. It will be shown that symmetry can be used to study the locomotion of this system. Using symmetry, a technique will be developed to obtain periodic solutions for each of the gaits of the quadruped model. These periodic solutions will be computed at various speeds. The energy levels will be compared for each of the gaits. The exchange of energy between its different forms will be shown for different gaits. It will be shown that even without body flexibility, there are significant savings in energy due to gait transition from trot to gallop. The energy levels will be used to predict the trot-gallop transition speed. These results will be compared with the experimental results for horses and dogs.
Gu, F & Dissanayake, MWMG 1970, 'Neural networks for modelling robot forward dynamics', IEEE International Conference on Neural Networks - Conference Proceedings, 1995 IEEE International Conference on Neural Networks (ICNN 95), IEEE, UNIV W AUSTRAIA, PERTH, AUSTRALIA, pp. 2715-2719.
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Accurate identification of robot dynamics is essential for the implementation of advanced robot control algorithms. The nonlinearities present in a typical robot system make it difficult to use existing linear methods for this purpose. In this paper a new approach where feedforward neural networks are employed for robot system identification is presented. It is shown that the neural network model can accurately predict the behaviour of the robot system. Dynamic model of a two link IBM robot is obtained using data generated by computer simulation, to illustrate the proposed method.
