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

Displacement-based grasping of deformable objects

Robotic grasping of deformable objects is inherently different from that of rigid objects, and is an under-researched area. Difficulties arise not only from expensive deformable modeling, but also from the changing object geometry under grasping force. This dissertation studies strategies of grasping deformable objects using two robotic fingers. Discovering the inapplicability of the traditional force-centered grasping strategies for rigid objects, I have designed an approach for grasping deformable objects that specifies finger displacements. This not only ensures equilibrium under the elasticity theory, but also enhances stability and simplifies finger control in the implementation. Deformable modeling is carried out using the Finite Element Method (FEM), for which our analysis establishes uniqueness of the shape of a grasped object given the finger displacements. Meanwhile, preprocessing based on the Singular Value Decomposition greatly reduces the complexity of computation. Grasping strategies have been investigated for both 2D and 3D objects. With a hollow 2D object, the grasping fingers make point contacts. The condition of a successful grasp is that the friction cones at the two contacts must contain the line segment through them before and after the deformation. With a solid planar object, the fingers make area contacts. Grasp computation is carried out by an event-driven algorithm, which has been validated by our robot experiments. For 3D objects, a simple squeeze-and-test strategy has been designed to lift them off the supporting table against gravity with a method that predicts the squeeze amounts. In reality, objects shapes are affected to various degrees by gravity, but such a effect has been ignored in the FEM-based modeling. For accuracy, the gravity-free shape of an object is sometimes needed. I have introduced an iterative algorithm that will converge to such shape as a fixed point . In the last part of my thesis, I study planning of the finger squeeze paths, not to limited by straight movements. The objective is to not only enlarge the range of finger placements for successful grasps, but also improve stability and energy efficiency. I have designed a path planning algorithm based on the Rapidly-exploring Random Trees (RRT) that is able to achieve certain optimalities