## Task-Directed Sensor Fusion and Planning: A Computational ApproachIf 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 |

247 | |

250 | |

### Other editions - View all

Task-Directed Sensor Fusion and Planning: A Computational Approach Gregory D. Hager Limited preview - 2012 |

Task-Directed Sensor Fusion and Planning: A Computational Approach Gregory D. Hager No preview available - 2013 |

Task-Directed Sensor Fusion and Planning: A Computational Approach Gregory D. Hager No preview available - 2013 |

### Common terms and phrases

action additional algorithm approximation approximation error Bayes Bayes theorem calibration error camera Chapter components compute constraints convergence coordinate system corner correspondence parameters covariance covariance matrix decision rule decision theory define described description vector determine discrete discrete space discuss Durrant-Whyte dynamic effect estimate evaluating example Figure filter function Gaussian geometric model given grid element grid-based method implemented information gathering information-gathering integral Iterations linear matrix maximizing mean-square minimax MMSE model parameters modeling error motion nonlinear object observation error optimal parameter space parameter vector partition payoff performance piecewise-constant position possible posterior prior distribution probability distribution problem procedure projection grid random variable relative representation requires robot robust rotation sampling distribution scalar sensor description sensor fusion sensor model sensor observation sensor system solution statistical structure superellipsoid superquadric tactile sensor task model techniques tion tolerance tolerance interval transformation uncertainty unknown parameters updating utility variation

### References to this book

Directed Sonar Sensing for Mobile Robot Navigation John J. Leonard,Hugh F. Durrant-Whyte No preview available - 1992 |

Multisensor Fusion: A Minimal Representation Framework Rajive Joshi,Arthur C. Sanderson Limited preview - 1999 |