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Preface |
6 |
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Acknowledgements |
7 |
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Contents |
8 |
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1 Introduction |
17 |
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1.1 Overview |
17 |
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1.2 Collaboration Between One Human--Robot Pair |
20 |
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1.3 Collaboration Between Human and Multiple Robots/Swarms |
22 |
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References |
24 |
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2 Robust Shared-Control for Rear-Wheel Drive Cars |
30 |
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2.1 Introduction |
30 |
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2.2 Problem Formulation, Definitions, and Assumptions |
31 |
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2.3 Design of the Shared-Control Law with Measurements of Absolute Positions |
34 |
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2.3.1 Design of the Feedback Controller |
34 |
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2.3.2 Shared-Control Algorithm |
37 |
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2.4 Disturbance Rejections |
40 |
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2.5 Design of the Shared Control Without Measurements of Absolute Positions |
43 |
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2.5.1 Design of the Feedback Controller |
44 |
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2.5.2 Shared-Control Algorithm |
46 |
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2.6 Case Studies |
48 |
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2.6.1 Case I: Turning Without Absolute Positioning |
48 |
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2.6.2 Case II: Driving on a Road with Parked Cars |
51 |
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2.6.3 Case III: Emergency Breaking |
51 |
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2.7 Conclusions |
53 |
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References |
53 |
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3 Baxter-On-Wheels (BOW): An Assistive Mobile Manipulator for Mobility Impaired Individuals |
56 |
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3.1 Introduction |
56 |
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3.2 System Description |
59 |
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3.2.1 Experimental Platform: BOW |
59 |
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3.2.2 System Kinematics |
63 |
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3.3 Control Algorithm |
64 |
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3.3.1 Baseline Shared-Control Algorithm |
64 |
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3.3.2 Free-Space Mode and Contact Mode |
66 |
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3.4 Application to the BOW |
69 |
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3.4.1 User Interface |
69 |
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3.4.2 Object Pick-Up and Placement Task |
70 |
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3.4.3 Board Cleaning Task |
73 |
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3.5 Conclusion |
75 |
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References |
76 |
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4 Switchings Between Trajectory Tracking and Force Minimization in Human--Robot Collaboration |
79 |
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4.1 Introduction |
79 |
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4.2 Dynamic Models |
81 |
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4.2.1 Robot Model |
82 |
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4.2.2 Human Arm Model |
82 |
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4.2.3 Unified Model |
84 |
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4.2.4 Trajectory Tracking |
85 |
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4.3 Control Design |
85 |
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4.3.1 Control Objective |
85 |
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4.3.2 Selection of Cost Functions |
86 |
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4.3.3 Optimal Control |
87 |
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4.4 Simulations |
89 |
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4.4.1 Simulation Settings |
89 |
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4.4.2 Change of Weights |
90 |
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4.4.3 Adaptation of Desired Trajectory |
92 |
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4.5 Conclusions |
93 |
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References |
94 |
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5 Estimating Human Intention During a Human--Robot Cooperative Task Based on the Internal Force Model |
96 |
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5.1 Introduction |
96 |
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5.2 Internal Force Model |
99 |
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5.2.1 Problem Formulation |
99 |
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5.2.2 Existing Models |
100 |
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5.2.3 Proposed Model |
101 |
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5.2.4 Discussion |
104 |
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5.3 Method |
104 |
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5.3.1 Apparatus |
105 |
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5.3.2 Procedure |
105 |
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5.4 Results |
107 |
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5.5 Validation of the Model |
110 |
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5.6 Statistical Analysis of the Internal Force Features |
112 |
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5.6.1 Initial Grasp Force Magnitude |
113 |
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5.6.2 Final Grasp Force Magnitude |
114 |
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5.6.3 Internal Force Energy |
114 |
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5.6.4 Difference Between Initial and Final Grasp Forces |
115 |
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5.6.5 Internal Force Variation |
115 |
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5.6.6 Negotiation Force |
116 |
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5.6.7 Negotiation Force Versus Object Velocity |
117 |
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5.7 Proposed Cooperation Policy |
118 |
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5.8 Conclusion |
120 |
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References |
121 |
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6 A Learning Algorithm to Select Consistent Reactions to Human Movements |
123 |
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6.1 Introduction |
123 |
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6.2 Background |
125 |
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6.2.1 Expert-Based Learning |
125 |
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6.2.2 Binary Learning Algorithms |
126 |
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6.3 Analysis |
127 |
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6.3.1 Performance |
128 |
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6.3.2 Consistency |
128 |
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6.3.3 Adaptiveness |
130 |
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6.3.4 Tie Breaking |
131 |
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6.4 Expanded Dual Expert Algorithm |
132 |
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6.4.1 Performance Analysis |
133 |
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6.4.2 Consistency and Adaptiveness |
134 |
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6.5 Simulation |
134 |
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6.5.1 Dual Expert Algorithm |
134 |
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6.5.2 Expanded Dual Expert Algorithm |
135 |
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6.6 Experiment |
137 |
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6.6.1 Setup |
138 |
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6.6.2 Results |
139 |
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6.7 Conclusions |
141 |
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References |
141 |
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7 Assistive Optimal Control-on-Request with Application in Standing Balance Therapy and Reinforcement |
143 |
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7.1 Introduction |
143 |
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7.2 Assistive Control Synthesis |
145 |
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7.2.1 Calculating a Schedule of Optimal Infinitesimal Actions |
145 |
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7.2.2 Computing the Control Duration |
150 |
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7.3 Human--Robot Interaction in Assisted Balance Therapy |
151 |
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7.3.1 Related Work: Assist-as-Needed Techniques |
152 |
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7.3.2 Interactive Simulation Study |
153 |
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7.4 Human--Robot Communication in Posture Reinforcement: A Short Study |
157 |
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7.5 Conclusion |
160 |
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References |
161 |
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8 Intelligent Human--Robot Interaction Systems Using Reinforcement Learning and Neural Networks |
164 |
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8.1 Introduction |
164 |
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8.2 HRI Control: Motivation and Structure Overview of the Proposed Approach |
166 |
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8.3 Inner Robot-Specific Loop |
167 |
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8.4 Outer Task-Specific Loop Control |
173 |
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8.4.1 Task-Specific Outer Loop Control Method: An LQR Approach |
173 |
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8.4.2 Learning Optimal Parameters of the Prescribed Impedance Model Using Integral Reinforcement Learning |
177 |
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8.5 Simulation Results |
178 |
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8.6 Conclusion |
185 |
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References |
185 |
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9 Regret-Based Allocation of Autonomy in Shared Visual Detection for Human--Robot Collaborative Assembly in Manufacturing |
188 |
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9.1 Introduction |
188 |
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9.2 The Hybrid Cell for Human--Robot Collaborative Assembly |
190 |
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9.3 Detection Problem Formulation with Focus on the Selected Assembly Task |
193 |
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9.3.1 Description of the Problem |
193 |
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9.3.2 Problem Formulation |
196 |
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9.4 Bayesian Sequential Decision-Making Algorithm for Allocation of Autonomy |
197 |
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9.5 Inclusion of Regret in Bayesian Decision-Making Algorithm for Allocation of Autonomy |
198 |
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9.6 Illustration of the Decision-Making Approach |
201 |
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9.6.1 Illustration of the Optimal Bayesian Decision-Making Approach |
201 |
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9.6.2 Illustration of the Regret-Based Modified Decision-Making Approach |
204 |
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9.7 Implementation Scheme of the Regret-Based Bayesian Decision-Making Approach for the Assembly Task |
204 |
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9.7.1 The Overall Scheme in a Flowchart |
204 |
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9.7.2 Measurement of Sensing Probability and Observation Cost |
207 |
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9.7.3 Measurement Method for Regret Intensity |
208 |
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9.8 Experimental Evaluation of the Regret-Based Bayesian Decision-Making Approach |
210 |
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9.8.1 Objective |
210 |
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9.8.2 Hypothesis |
210 |
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9.8.3 The Evaluation Criteria |
211 |
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9.8.4 The Experiment Design |
211 |
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9.8.5 Subjects |
211 |
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9.8.6 The Experimental Procedures |
212 |
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9.9 Evaluation Results and Analyses |
212 |
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9.10 Conclusions and Future Innovations |
214 |
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References |
215 |
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10 Considering Human Behavior Uncertainty and Disagreements in Human--Robot Cooperative Manipulation |
217 |
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10.1 Introduction |
217 |
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10.2 Human--Robot Cooperative Manipulation |
219 |
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10.2.1 Cooperative Manipulation |
219 |
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10.2.2 Control Challenges in Physical Human--Robot Interaction |
222 |
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10.2.3 Reactive Assistants |
222 |
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10.2.4 Proactive Assistants |
223 |
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10.3 Interaction Wrench Decomposition |
225 |
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10.3.1 Nonuniform Wrench Decomposition Matrices |
226 |
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10.3.2 Effective and Internal Wrenches |
227 |
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10.3.3 Load Share and Disagreement |
231 |
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10.4 Optimal Robot Assistance Considering Human Behavior Uncertainty and Disagreements |
231 |
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10.4.1 Anticipatory Assistance Based on Learned Models |
232 |
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10.4.2 The Two-Dimensional Translational Case |
237 |
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10.4.3 Experiments |
239 |
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10.5 Conclusions |
245 |
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References |
248 |
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11 Designing the Robot Behavior for Safe Human--Robot Interactions |
251 |
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11.1 Introduction |
251 |
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11.1.1 The Safety Issues and Existing Solutions |
252 |
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11.1.2 Safety Problems in HRI: Conflicts in Multiagent Systems |
252 |
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11.1.3 Safe Control and Exploration |
253 |
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11.2 Modeling the Human--Robot Interactions |
254 |
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11.2.1 The Agent Model |
254 |
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11.2.2 The Closed-Loop System |
255 |
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11.2.3 Information Structure |
256 |
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11.3 The Safety-Oriented Behavior Design |
257 |
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11.3.1 The Safety Principle |
257 |
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11.3.2 The Safety Index |
258 |
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11.4 The Safe Set Algorithm (SSA) |
260 |
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11.4.1 The Control Algorithm |
261 |
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11.4.2 Online Learning and Prediction of Humans' Dynamics |
262 |
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11.4.3 Applications |
263 |
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11.5 The Safe Exploration Algorithm (SEA) |
265 |
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11.5.1 The Safe Set in the Belief Space |
266 |
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11.5.2 Learning in the Belief Space |
267 |
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11.5.3 A Comparative Study Between SSA and SEA |
270 |
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11.6 Combining SSA and SEA in Time Varying MAS Topology |
273 |
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11.6.1 The Control Algorithm |
274 |
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11.6.2 The Learning Algorithm |
275 |
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11.6.3 Performance |
275 |
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11.7 Discussions |
276 |
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11.7.1 The Energy Based Methods |
277 |
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11.7.2 Limitations and Future Work |
277 |
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11.8 Conclusion |
278 |
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References |
278 |
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12 When Human Visual Performance Is Imperfect---How to Optimize the Collaboration Between One Human Operator and Multiple Field Robots |
281 |
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12.1 Introduction |
281 |
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12.2 Human and Robot Performance in Target Classification [4] |
283 |
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12.3 Optimizing Human--Robot Collaboration for Target Classification |
285 |
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12.3.1 Predetermined Site Allocation |
285 |
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12.3.2 Optimized Site Allocation |
290 |
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12.4 Numerical Results |
293 |
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12.4.1 Collaboration Between the Human Operator and One Robot [14] |
294 |
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12.4.2 Predetermined Site Allocation |
296 |
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12.4.3 Optimized Site Allocation |
303 |
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12.5 Conclusions |
308 |
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References |
308 |
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13 Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots |
310 |
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13.1 Introduction |
310 |
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13.2 Modeling of the Robot and the Interactions |
311 |
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13.2.1 Mobile Robot |
311 |
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13.2.2 Communication Infrastructure |
313 |
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13.2.3 Human--Robot Interface |
313 |
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13.3 A Taxonomy of Collaborative Human--Robot Control |
315 |
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13.3.1 Physical Domain of the Robots |
315 |
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13.3.2 Degree of Autonomy from the Human Operator |
316 |
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13.3.3 Force Interaction with the Operator |
319 |
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13.3.4 Near-Operation Versus Teleoperation |
321 |
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13.3.5 Physical Interaction with the Environment |
322 |
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13.3.6 Use of Onboard Sensors Only |
324 |
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13.4 A Taxonomy of Collaborative Human--Multi-robot Control |
325 |
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13.4.1 Level of Centralization |
325 |
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13.4.2 Master--Leader--Followers Schemes |
326 |
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13.4.3 Formation-Orthogonal Control Schemes |
327 |
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13.4.4 Group-Property Preservation Schemes |
328 |
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13.4.5 Physical Interaction with Contact |
329 |
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13.5 Conclusions |
330 |
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References |
331 |
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14 A Passivity-Based Approach to Human--Swarm Collaboration and Passivity Analysis of Human Operators |
334 |
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14.1 Introduction |
334 |
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14.2 Intended Scenario and Control Goals |
337 |
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14.3 Control Architecture and Passivity |
340 |
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14.4 Convergence Analysis |
342 |
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14.4.1 Synchronization in Position Control Mode |
342 |
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14.4.2 Synchronization in Velocity Control Mode |
346 |
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14.5 Passivity of the Human Operator Decision Process |
349 |
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14.5.1 Experimental Setup and Approach |
350 |
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14.5.2 Analysis on Human Passivity in Position Control Mode |
353 |
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14.5.3 Analysis on Human Passivity in Velocity Control Mode |
356 |
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14.5.4 Analysis on Individual Variability |
358 |
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14.6 Summary |
362 |
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References |
363 |
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15 Human--Swarm Interactions via Coverage of Time-Varying Densities |
365 |
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15.1 Introduction |
365 |
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15.2 Human--Swarm Interactions via Coverage |
367 |
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15.2.1 The Coverage Problem |
370 |
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15.2.2 Centralized Coverage of Time-Varying Densities |
373 |
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15.2.3 Distributed Coverage of Time-Varying Densities |
377 |
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15.3 Designing Density Functions |
382 |
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15.3.1 Diffusion of Drawn Geometric Configurations |
382 |
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15.3.2 Control of Gaussian Functions |
384 |
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15.4 Robotic Experiments |
385 |
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15.5 Conclusions |
388 |
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References |
389 |
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16 Co-design of Control and Scheduling for Human--Swarm Collaboration Systems Based on Mutual Trust |
394 |
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16.1 Introduction |
394 |
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16.2 Swarm Setup |
397 |
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16.2.1 Dynamic Timing Model and Collaboration Delay |
397 |
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16.2.2 Cooperative Control for Swarm Agents |
399 |
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16.3 Collaboration Framework |
405 |
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16.3.1 Trust Model |
405 |
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16.3.2 Human Performance Model |
406 |
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16.3.3 Swarm Performance Model |
407 |
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16.3.4 Human Attention Preference |
407 |
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16.3.5 Fitness |
409 |
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16.4 Real-Time Scheduling |
410 |
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16.5 Simulation Results |
411 |
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16.5.1 Parameter Setup |
411 |
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16.5.2 Results and Discussions |
413 |
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16.6 Conclusions |
417 |
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References |
418 |
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Index |
421 |
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