Task-Directed Sensor Fusion and Planning: A Computational Approach

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Springer Science & Business Media, May 31, 1990 - Technology & Engineering - 254 pages
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If you have ever hiked up a steep hill to reach a viewpoint, you will know that sensing can involve the expenditure of effort. More generally, the choice of which movement an intelligent system chooses to make is usually based on information gleaned from sensors. But the information required to make the motion decision may not be immediately to hand, so the system . first has to plan a motion whose purpose is to acquire the needed sensor information. Again, this conforms to our everyday experience: I am in the woods and don't know which direction to go, so I climb up to the ridge to get my bearings; I am lost in a new town, so I plan to drive to the next junction where there is sure to be a roadsign, failing that I will ask someone who seems to be from the locality. Why, if experiences such as these are so familiar, has the problem only recently been recognised and studied in Robotics? One reason is that until quite recently Robotics research was dominated by work on robot arms with limited reach and fixed in a workcell.
 

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Contents

Introduction
1
11 A Model for Information Gathering
3
12 A Strategy for Realizing Information Gathering
7
13 Organizations for Information Gathering
8
14 An Overview of this Book
11
15 Literature
13
Modeling Sensors
15
21 Modeling Sensing Geometry
17
63 Simulation Analysis of Sensor Planning
146
64 Discussion and Extensions
151
65 Literature
152
Towards a TaskLevel Programming Environment
155
71 Sensor Fusion
157
72 Task Specification
166
73 Observation Planning
169
74 Summary and Future Development
173

22 Modeling Sensor Observation Uncertainty
30
23 Additional Modeling Considerations
40
24 An Example System
42
25 Discussion
48
26 Literature
51
Task Modeling and Decision Making
53
31 Task Modeling
54
32 Decision Theory
62
33 Discussion
68
34 Literature
70
MeanSquare Estimation
73
41 Derivation of Mean Square Estimation Techniques
75
42 Robustness to System Variation
81
43 Robust Rules for Nonlinear Systems
89
44 Additional Comments On MomentBased Representations
101
45 Discussion
104
46 Literature
105
GridBased Probability Density Methods
107
51 GridBased Probability Density Updating
109
52 Estimation and Payoff Computation
118
53 Robustness
120
54 Error Analysis
125
55 Simulation Evaluation
129
56 Extensions
133
57 Discussion
134
58 Literature
136
Choosing Viewpoints and Features
137
61 Describing the Sensor Action Space
138
62 Implementing Sensor Planning
143
75 Literature
174
An Experimental System
175
82 Experimental Results
189
83 Discussion
197
Future Extensions
199
91 System Organization
200
92 Information Gathering with Multiple Sensors
202
93 The Model Selection Problem
205
94 Sensor Fusion and Artificial Intelligence
206
95 Summary
208
Review of Probability
211
A2 Conditional Probability
214
A3 Expectations
215
A4 Transforming Probability
217
A5 Convergence
218
Review of Methods for Estimation
221
B1 Stochastic Approximation
222
B2 Least Squares Methods
223
B3 Maximum Likelihood Method
224
B5 Decision Theory1
225
B6 Game Theory
227
B7 rmaximin Game Theory
229
System Hardware
231
References
233
Glossary of Mathematical Notation
245
Glossary of Symbols
247
Index
250
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