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E-raamat: In-Hand Object Localization and Control: Enabling Dexterous Manipulation with Robotic Hands

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This book introduces a novel model-based dexterous manipulation framework, which, thanks to its precision and versatility, significantly advances the capabilities of robotic hands compared to the previous state of the art. This is achieved by combining a novel grasp state estimation algorithm, the first to integrate information from tactile sensing, proprioception and vision, with an impedance-based in-hand object controller, which enables leading manipulation capabilities, including finger gaiting. The developed concept is implemented on one of the most advanced robotic manipulators, the DLR humanoid robot David, and evaluated in a range of challenging real-world manipulation scenarios and tasks. This book greatly benefits researchers in the field of robotics that study robotic hands and dexterous manipulation topics, as well as developers and engineers working on industrial automation applications involving grippers and robotic manipulators.


1 Introduction
1(14)
1.1 Dexterous Manipulation
4(5)
1.2 Contribution
9(3)
1.3 Organization of this Work
12(3)
References
14(1)
2 Related Work
15(18)
2.1 Dexterous Robotic Hands
15(5)
2.1.1 A Brief History of Robotic Hands
15(3)
2.1.2 DLR David
18(2)
2.2 Dexterous Manipulation
20(13)
2.2.1 Overview
20(2)
2.2.2 Grasp State Estimation
22(3)
2.2.3 Impedance-Based Object Control
25(1)
2.2.4 Learning-Based Methods
26(1)
References
27(6)
3 Grasp Modeling
33(24)
3.1 Definitions
33(4)
3.2 Kinematics
37(9)
3.2.1 Forward Kinematics
37(2)
3.2.2 Grasp Matrix
39(2)
3.2.3 Hand Jacobian
41(2)
3.2.4 Contact Model
43(3)
3.3 Dynamics
46(4)
3.3.1 Rigid Body Dynamics
46(2)
3.3.2 Grasp Dynamics
48(1)
3.3.3 Contact Dynamics
49(1)
3.4 Grasp Subspaces
50(2)
3.5 Types of Grasps
52(5)
References
55(2)
4 Grasp State Estimation
57(68)
4.1 Introduction
57(6)
4.1.1 Concept
58(3)
4.1.2 Problem Statement
61(2)
4.2 Probabilistic Grasp State Estimation
63(7)
4.2.1 Fundamentals
63(1)
4.2.2 Particle Filter
64(3)
4.2.3 Extended Kalman Filter
67(2)
4.2.4 Filter Selection
69(1)
4.3 Contact Detection and Localization
70(5)
4.3.1 Collision Detection
70(1)
4.3.2 Joint Torque Measurements
71(1)
4.3.3 Contact Point Localization
72(3)
4.4 State Estimation from Finger Position Measurements
75(11)
4.4.1 Grasp State Definition
76(1)
4.4.2 Motion Model
76(3)
4.4.3 Measurement Model
79(2)
4.4.4 Extensions
81(5)
4.5 Data Fusion with Fiducial Markers
86(6)
4.5.1 AprilTag
86(2)
4.5.2 Measurement Model
88(1)
4.5.3 Camera Localization
89(2)
4.5.4 Target Tracking
91(1)
4.6 Data Fusion with Contour Features
92(5)
4.6.1 Feature Extraction
93(3)
4.6.2 Measurement Model
96(1)
4.7 Data Fusion with Visual Object Tracking
97(3)
4.7.1 Multi-Modality Visual Object Tracking
98(1)
4.7.2 Measurement Model
99(1)
4.8 Data Fusion Under Measurement Delays
100(5)
4.9 Experimental Validation
105(15)
4.9.1 Grasp Acquisition
105(8)
4.9.2 Pick-and-Place
113(3)
4.9.3 In-Hand Manipulation
116(4)
4.10 Summary
120(5)
References
122(3)
5 Impedance-Based Object Control
125(48)
5.1 Introduction
125(6)
5.1.1 Concept
126(2)
5.1.2 Problem Statement
128(3)
5.2 Controller Design
131(5)
5.2.1 Object Impedance
131(3)
5.2.2 Force Distribution
134(1)
5.2.3 Architecture Overview
135(1)
5.3 Object Impedance Control
136(3)
5.3.1 Object Positioning
136(2)
5.3.2 Maintaining the Grasp Configuration
138(1)
5.4 Internal Forces
139(8)
5.4.1 Force Distribution
140(4)
5.4.2 Quadratic Optimization
144(1)
5.4.3 Extensions
145(2)
5.5 Torque Mapping
147(2)
5.5.1 Force Mapping
147(1)
5.5.2 Nullspace Control
148(1)
5.6 Grasp Reconfiguration
149(4)
5.6.1 Adding and Removing Contacts
150(1)
5.6.2 Grasp Acquisition
151(2)
5.7 Enabling In-Hand Manipulation
153(4)
5.7.1 Finger Gaiting Interface
153(2)
5.7.2 Contact Point Relocation
155(2)
5.8 Experimental Validation
157(12)
5.8.1 Tracking Performance
158(2)
5.8.2 Stabilizing the Grasp Acquisition
160(7)
5.8.3 Finger Gaiting
167(2)
5.9 Summary
169(4)
References
170(3)
6 Conclusion
173
6.1 Summary and Discussion
173(4)
6.2 Outlook
177
References
180