Dynamics, control and sensor issues pertinent to robotic hands for the EVA retriever system

Basic dynamics, sensor, control, and related artificial intelligence issues pertinent to smart robotic hands for the Extra Vehicular Activity (EVA) Retriever system are summarized and discussed. These smart hands are to be used as end effectors on arms attached to manned maneuvering units (MMU). The Retriever robotic systems comprised of MMU, arm and smart hands, are being developed to aid crewmen in the performance of routine EVA tasks including tool and object retrieval. The ultimate goal is to enhance the effectiveness of EVA crewmen

Mechanical design optimization for multi-finger haptic devices applied to virtual grasping manipulation

This paper describes the design of a modular multi-finger haptic device for virtual object manipulation. Mechanical structures are based on one module per finger and can be scaled up to three fingers. Mechanical configurations for two and three fingers are based on the use of one and two redundant axes, respectively. As demonstrated, redundant axes significantly increase workspace and prevent link collisions, which is their main asset with respect to other multi-finger haptic devices. The location of redundant axes and link dimensions have been optimized in order to guarantee a proper workspace, manipulability, force capability, and inertia for the device. The mechanical haptic device design and a thimble adaptable to different finger sizes have also been developed for virtual object manipulation

Dynamic modeling and simulation of a multi-fingered robot hand.

by Joseph Chun-kong Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 117-124).Abstract also in Chinese.Abstract --- p.iAcknowledgments --- p.ivList of Figures --- p.xiList of Tables --- p.xiiList of Algorithms --- p.xiiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related Work --- p.5Chapter 1.3 --- Contributions --- p.7Chapter 1.4 --- Organization of the Thesis --- p.9Chapter 2 --- Contact Modeling: Kinematics --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Contact Kinematics between Two Rigid Bodies --- p.14Chapter 2.2.1 --- Contact Modes --- p.14Chapter 2.2.2 --- Montana's Contact Equations --- p.15Chapter 2.3 --- Finger Kinematics --- p.18Chapter 2.3.1 --- Finger Forward Kinematics --- p.19Chapter 2.3.2 --- Finger Jacobian --- p.21Chapter 2.4 --- Grasp Kinematics between a Finger and an Object --- p.21Chapter 2.4.1 --- Velocity Transformation between Different Coordinate Frames --- p.22Chapter 2.4.2 --- Grasp Kinematics for the zth Contact --- p.23Chapter 2.4.3 --- Different Fingertip Models and Different Contact Modes --- p.25Chapter 2.5 --- Velocity Constraints of the Entire System --- p.28Chapter 2.6 --- Summary --- p.29Chapter 3 --- Contact Modeling: Dynamics --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Multi-fingered Robot Hand Dynamics --- p.33Chapter 3.3 --- Object Dynamics --- p.35Chapter 3.4 --- Constrained System Dynamics --- p.37Chapter 3.5 --- Summary --- p.39Chapter 4 --- Collision Modeling --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Assumptions of Collision --- p.42Chapter 4.3 --- Collision Point Velocities --- p.43Chapter 4.3.1 --- Collision Point Velocity of the ith. Finger --- p.43Chapter 4.3.2 --- Collision Point Velocity of the Object --- p.46Chapter 4.3.3 --- Relative Collision Point Velocity --- p.47Chapter 4.4 --- Equations of Collision --- p.47Chapter 4.4.1 --- Sliding Mode Collision --- p.48Chapter 4.4.2 --- Sticking Mode Collision --- p.49Chapter 4.5 --- Summary --- p.51Chapter 5 --- Dynamic Simulation --- p.53Chapter 5.1 --- Introduction --- p.53Chapter 5.2 --- Architecture of the Dynamic Simulation System --- p.54Chapter 5.2.1 --- Input Devices --- p.54Chapter 5.2.2 --- Dynamic Simulator --- p.58Chapter 5.2.3 --- Virtual Environment --- p.60Chapter 5.3 --- Methodologies and Program Flow of the Dynamic Simulator --- p.60Chapter 5.3.1 --- Interference Detection --- p.61Chapter 5.3.2 --- Constraint-based Simulation --- p.63Chapter 5.3.3 --- Impulse-based Simulation --- p.66Chapter 5.4 --- Summary --- p.69Chapter 6 --- Simulation Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Change of Grasping Configurations --- p.71Chapter 6.3 --- Rolling Contact --- p.76Chapter 6.4 --- Sliding Contact --- p.76Chapter 6.5 --- Collisions --- p.85Chapter 6.6 --- Dextrous Manipulation Motions --- p.93Chapter 6.7 --- Summary --- p.94Chapter 7 --- Conclusions --- p.99Chapter 7.1 --- Summary of Contributions --- p.99Chapter 7.2 --- Future Work --- p.100Chapter 7.2.1 --- Improvement of Current System --- p.100Chapter 7.2.2 --- Applications --- p.101Chapter A --- Montana's Contact Equations for Finger-object Contact --- p.103Chapter A.1 --- Local Coordinates Charts --- p.103Chapter A.2 --- "Curvature, Torsion and Metric Tensors" --- p.104Chapter A.3 --- Montana's Contact Equations --- p.106Chapter B --- Finger Dynamics --- p.108Chapter B.1 --- Forward Kinematics of a Robot Finger --- p.108Chapter B.1.1 --- Link-coordinate Transformation --- p.109Chapter B.1.2 --- Forward Kinematics --- p.109Chapter B.2 --- Dynamic Equation of a Robot Finger --- p.110Chapter B.2.1 --- Kinetic and Potential Energy --- p.110Chapter B.2.2 --- Lagrange's Equation --- p.111Chapter C --- Simulation Configurations --- p.113Chapter C.1 --- Geometric models --- p.113Chapter C.2 --- Physical Parameters --- p.113Chapter C.3 --- Simulation Parameters --- p.116Bibliography --- p.12

MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

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