Grasping Force Prediction for Underactuated Multi-Fingered Hand by Using Artificial Neural Network

In this paper, the feedforward neural network with Levenberg-Marquardt backpropagation training algorithm is used to predict the grasping forces according to the multisensory signals as training samples for specific design of underactuated multifingered hand to avoid the complexity of calculating the inverse kinematics which is appeared through the dynamic modeling of the robotic hand and preparing this network to be used as part of a control system.Keywords: Grasping force, underactuated, prediction, Neural networ

Measuring inefficiency in the rubber manufacturing industry

Malaysia is the fifth largest producer of natural rubber in the world after Thailand, Indonesia, Vietnam and China as well as producing rubber products exported to more than 190 countries worldwide. However, the slowdown in growth of major importers such as China, the European Union and the United States and the perception of stock surplus as output exceeds demand led to fluctuating rubber production performance over the period 2010 to 2016. Hence, this article aims at examining the level of technical efficiency (TE) and to analyze the determinants of the inefficiencies of the rubber manufacturing industry. The analysis was conducted using the latest 145 firms’ data obtained from the Department of Statistics Malaysia (DOS) and using the Stochastic Frontier Analysis (SFA) method. The results showed that the overall TE level was high while the determinants such as the capital-labor ratio, wage rate and firm size had a negative and significant impact that could reduce industrial technical efficiencies. The policy implication is that the rubber manufacturing industry needs to focus on high technological production investment, increase employee motivation through wage increment and create more strategic cooperation with international industry

Recurrent neural networks for force optimization of multi-fingered robotic hands.

Fok Lo Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 133-135).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Multi-fingered Robotic Hands --- p.1Chapter 1.2 --- Grasping Force Optimization --- p.2Chapter 1.3 --- Neural Networks --- p.6Chapter 1.4 --- Previous Work for Grasping Force Optimization --- p.9Chapter 1.5 --- Contributions of this work --- p.10Chapter 1.6 --- Organization of this thesis --- p.12Chapter 2. --- Problem Formulations --- p.13Chapter 2.1 --- Grasping Force Optimization without Joint Torque Limits --- p.14Chapter 2.1.1 --- Linearized Friction Cone Approach --- p.15Chapter i. --- Linear Formulation --- p.17Chapter ii. --- Quadratic Formulation --- p.18Chapter 2.1.2 --- Nonlinear Friction Cone as Positive Semidefinite Matrix --- p.19Chapter 2.1.3 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.20Chapter 2.2 --- Grasping Force Optimization with Joint Torque Limits --- p.21Chapter 2.2.1 --- Linearized Friction Cone Approach --- p.23Chapter 2.2.2 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.23Chapter 2.3 --- Grasping Force Optimization with Time-varying External Wrench --- p.24Chapter 2.3.1 --- Linearized Friction Cone Approach --- p.25Chapter 2.3.2 --- Nonlinear Friction Cone as Positive Semidefinite Matrix --- p.25Chapter 2.3.3 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.26Chapter 3. --- Recurrent Neural Network Models --- p.27Chapter 3.1 --- Networks for Grasping Force Optimization without Joint Torque LimitsChapter 3.1.1 --- The Primal-dual Network for Linear Programming --- p.29Chapter 3.1.2 --- The Deterministic Annealing Network for Linear Programming --- p.32Chapter 3.1.3 --- The Primal-dual Network for Quadratic Programming --- p.34Chapter 3.1.4 --- The Dual Network --- p.35Chapter 3.1.5 --- The Deterministic Annealing Network --- p.39Chapter 3.1.6 --- The Novel Network --- p.41Chapter 3.2 --- Networks for Grasping Force Optimization with Joint Torque LimitsChapter 3.2.1 --- The Dual Network --- p.43Chapter 3.2.2 --- The Novel Network --- p.45Chapter 3.3 --- Networks for Grasping Force Optimization with Time-varying External WrenchChapter 3.3.1 --- The Primal-dual Network for Quadratic Programming --- p.48Chapter 3.3.2 --- The Deterministic Annealing Network --- p.50Chapter 3.3.3 --- The Novel Network --- p.52Chapter 4. --- Simulation Results --- p.54Chapter 4.1 --- Three-finger Grasping Example of Grasping Force Optimization without Joint Torque Limits --- p.54Chapter 4.1.1 --- The Primal-dual Network for Linear Programming --- p.57Chapter 4.1.2 --- The Deterministic Annealing Network for Linear Programming --- p.59Chapter 4.1.3 --- The Primal-dual Network for Quadratic Programming --- p.61Chapter 4.1.4 --- The Dual Network --- p.63Chapter 4.1.5 --- The Deterministic Annealing Network --- p.65Chapter 4.1.6 --- The Novel Network --- p.57Chapter 4.1.7 --- Network Complexity Analysis --- p.59Chapter 4.2 --- Four-finger Grasping Example of Grasping Force Optimization without Joint Torque Limits --- p.73Chapter 4.2.1 --- The Primal-dual Network for Linear Programming --- p.75Chapter 4.2.2 --- The Deterministic Annealing Network for Linear Programming --- p.77Chapter 4.2.3 --- The Primal-dual Network for Quadratic Programming --- p.79Chapter 4.2.4 --- The Dual Network --- p.81Chapter 4.2.5 --- The Deterministic Annealing Network --- p.83Chapter 4.2.6 --- The Novel Network --- p.85Chapter 4.2.7 --- Network Complexity Analysis --- p.87Chapter 4.3 --- Three-finger Grasping Example of Grasping Force Optimization with Joint Torque Limits --- p.90Chapter 4.3.1 --- The Dual Network --- p.93Chapter 4.3.2 --- The Novel Network --- p.95Chapter 4.3.3 --- Network Complexity Analysis --- p.97Chapter 4.4 --- Three-finger Grasping Example of Grasping Force Optimization with Time-varying External Wrench --- p.99Chapter 4.4.1 --- The Primal-dual Network for Quadratic Programming --- p.101Chapter 4.4.2 --- The Deterministic Annealing Network --- p.103Chapter 4.4.3 --- The Novel Network --- p.105Chapter 4.4.4 --- Network Complexity Analysis --- p.107Chapter 4.5 --- Four-finger Grasping Example of Grasping Force Optimization with Time-varying External Wrench --- p.109Chapter 4.5.1 --- The Primal-dual Network for Quadratic Programming --- p.111Chapter 4.5.2 --- The Deterministic Annealing Network --- p.113Chapter 4.5.3 --- The Novel Network --- p.115Chapter 5.5.4 --- Network Complexity Analysis --- p.117Chapter 4.6 --- Four-finger Grasping Example of Grasping Force Optimization with Nonlinear Velocity Variation --- p.119Chapter 4.5.1 --- The Primal-dual Network for Quadratic Programming --- p.121Chapter 4.5.2 --- The Deterministic Annealing Network --- p.123Chapter 4.5.3 --- The Novel Network --- p.125Chapter 5.5.4 --- Network Complexity Analysis --- p.127Chapter 5. --- Conclusions and Future Work --- p.129Publications --- p.132Bibliography --- p.133Appendix --- p.13

Recurrent Neural Networks for Solving Second-Order Cone Programs

Abstract This paper proposes using the neural networks to efficiently solve the secondorder cone programs (SOCP). To establish the neural networks, the SOCP is first reformulated as a second-order cone complementarity problem (SOCCP) with the Karush-KuhnTucker conditions of the SOCP. The SOCCP functions, which transform the SOCCP into a set of nonlinear equations, are then utilized to design the neural networks. We propose two kinds of neural networks with the different SOCCP functions. The first neural network uses the Fischer-Burmeister function to achieve an unconstrained minimization with a merit function. We show that the merit function is a Lyapunov function and this neural network is asymptotically stable. The second neural network utilizes the natural residual function with the cone projection function to achieve low computation complexity. It is shown to be Lyapunov stable and converges globally to an optimal solution 1 E-mail: [email protected] 2 Corresponding author, Member of Mathematics Division, National Center for Theoretical Sciences, Taipei Office. The author's work is partially supported by National Science Council of Taiwan. E-mail: [email protected] 3 E-mail: [email protected] 1 under some condition. The SOCP simulation results demonstrate the effectiveness of the proposed neural networks

Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht