| Preface | xv |
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| 1 | What Is a Robot?
Defining Robotics | 1 |
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| 2 | Where Do Robots Come From?
A Brief but Gripping History of Robotics | 7 |
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| 2.1 | Control Theory | 7 |
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| 2.2 | Cybernetics | 8 |
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| 2.2.1 | Grey Walter's Tortoise | 9 |
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| 2.2.2 | Braitenberg's Vehicles | 11 |
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| 2.3 | Artificial Intelligence | 13 |
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| 3 | What's in a Robot?
Robot Components | 19 |
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| 3.1 | Embodiment | 20 |
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| 3.2 | Sensing | 21 |
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| 3.3 | Action | 24 |
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| 3.4 | Brains and Brawn | 25 |
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| 3.5 | Autonomy | 26 |
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| 4 | Arms, Legs, Wheels, Tracks, and What Really Drives Them
Effectors and Actuators | 29 |
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| 4.1 | Active vs. Passive Actuation | 30 |
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| 4.2 | Types of Actuators | 31 |
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| 4.3 | Motors | 32 |
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| 4.3.1 | Direct Current (DC) Motors | 32 |
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| 4.3.2 | Gearing | 3 |
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| 4.3.3 | Servo Motors | 37 |
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| 4.4 | Degrees of Freedom | 38 |
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| 5 | Move It!
Locomotion | 47 |
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| 5.1 | Stability | 48 |
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| 5.2 | Moving and Gaits | 51 |
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| 5.3 | Wheels and Steering | 53 |
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| 5.4 | Staying on the Path vs. Getting There | 55 |
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| 6 | Grasping at Straws
Manipulation | 59 |
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| 6.1 | Endeffectors | 59 |
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| 6.2 | Teleoperation | 60 |
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| 6.3 | Why Is Manipulation Hard? | 62 |
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| 7 | What's Going On?
Sensors | 69 |
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| 7.1 | Levels of Processing | 73 |
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| 8 | Switch on the Light
Simple Sensors | 81 |
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| 8.1 | Passive vs. Active Sensors | 81 |
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| 8.2 | Switches | 82 |
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| 8.3 | Light Sensors | 84 |
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| 8.3.1 | Polarized Light | 86 |
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| 8.3.2 | Reflective Optosensors | 86 |
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| 8.3.3 | Reflectance Sensors | 88 |
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| 8.3.4 | Infra Red Light | 89 |
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| 8.3.5 | Modulation and Demodulation of Light | 90 |
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| 8.3.6 | Break Beam Sensors | 90 |
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| 8.3.7 | Shaft Encoders | 91 |
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| 8.4 | Resistive Position Sensors | 94 |
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| 8.4.1 | Potentiometers | 95 |
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| 9 | Sonars, Lasers, and Cameras
Complex Sensors | 97 |
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| 9.1 | Ultrasonic or Sonar Sensing | 97 |
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| 9.1.1 | Sonar Before and Beyond Robotics | 100 |
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| 9.1.2 | Specular Reflection | 101 |
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| 9.2 | Laser Sensing | 104 |
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| 9.3 | Visual Sensing | 107 |
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| 9.3.1 | Cameras | 108 |
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| 9.3.2 | Edge Detection | 110 |
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| 9.3.3 | Model-Based Vision | 112 |
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| 9.3.4 | Motion Vision | 113 |
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| 9.3.5 | Stereo Vision | 114 |
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| 9.3.6 | Texture, Shading, Contours | 115 |
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| 9.3.7 | Biological Vision | 116 |
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| 9.3.8 | Vision for Robots | 117 |
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| 10 | Stay in Control
Feedback Control | 121 |
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| 10.1 | Feedback or Closed Loop Control | 121 |
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| 10.2 | The Many Faces of Error | 122 |
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| 10.3 | An Example of a Feedback Control Robot | 124 |
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| 10.4 | Types of Feedback Control | 126 |
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| 10.4.1 | Proportional Control | 126 |
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| 10.4.2 | Derivative Control | 128 |
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| 10.4.3 | Integral Control | 129 |
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| 10.4.4 | PD and PID Control | 130 |
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| 10.5 | Feedforward or Open Loop Control | 131 |
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| 11 | The Building Blocks of Control
Control Architectures | 135 |
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| 11.1 | Who Needs Control Architectures? | 135 |
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| 11.2 | Languages for Programming Robots | 137 |
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| 11.3 | And the Architectures are... | 139 |
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| 11.3.1 | Time | 141 |
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| 11.3.2 | Modularity | 141 |
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| 11.3.3 | Representation | 142 |
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| 12 | What's in Your Head?
Representation | 145 |
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| 12.1 | The Many Ways to Make a Map | 146 |
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| 12.2 | What Can the Robot Represent? | 147 |
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| 12.3 | Costs of Representing | 148 |
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| 13 | Think Hard, Act Later
Deliberative Control | 151 |
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| 13.1 | What Is Planning? | 151 |
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| 13.2 | Costs of Planning | 154 |
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| 14 | Don't Think, React!
Reactive Control | 161 |
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| 14.1 | Action Selection | 166 |
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| 14.2 | Subsumption Architecture | 169 |
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| 14.3 | Herbert, or How to Sequence Behaviors Through the World | 172 |
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| 15 | Think and Act Separately, in Parallel
Hybrid Control | 177 |
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| 15.1 | Dealing with Changes in the World/Map/Task | 179 |
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| 15.2 | Planning and Replanning | 180 |
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| 15.4 | On-Line and Off-Line Planning | 182 |
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| 16 | Think the Way You Act
Behavior-Based Control | 187 |
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| 16.1 | Distributed Representation | 192 |
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| 16.2 | An Example: Distributed Mapping | 193 |
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| 16.2.1 | Toto the Robot | 194 |
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| 16.2.2 | Toto's Navigation | 194 |
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| 16.2.3 | Toto's Landmark Detection | 196 |
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| 16.2.4 | Toto's Mapping Behaviors | 197 |
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| 16.2.5 | Path Planning in Toto's Behavior Map | 200 |
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| 16.2.6 | Toto's Map-Following | 202 |
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| 17 | Making Your Robot Behave
Behavior Coordination | 207 |
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| 17.1 | Behavior Arbitration: Make a Choice | 207 |
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| 17.2 | Behavior Fusion: Sum It Up | 209 |
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| 18 | When the Unexpected Happens
Emergent Behavior | 215 |
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| 18.1 | An Example: Emergent Wall-Following | 215 |
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| 18.2 | The Whole Is Greater Than the Sum of Its Parts | 218 |
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| 18.3 | Components of Emergence | 218 |
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| 18.4 | Expect the Unexpected | 218 |
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| 18.5 | Predictability of Surprise | 219 |
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| 18.6 | Good vs. Bad Emergent Behavior | 220 |
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| 18.7 | Architectures and Emergence | 221 |
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| 19 | Going Places
Navigation | 223 |
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| 19.1 | Localization | 225 |
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| 19.2 | Search and Path Planning | 228 |
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| 19.3 | SLAM | 229 |
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| 19.4 | Coverage | 230 |
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| 20 | Go, Team!
Group Robotics | 233 |
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| 234 | Benefits of Teamwork | 20.1 |
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| 20.2 | Challenges of Teamwork | 236 |
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| 20.3 | Types of Groups and Teams | 237 |
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| 20.4 | Communication | 241 |
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| 20.4.1 | Kin Recognition | 246 |
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| 20.5 | Getting a Team to Play Together | 247 |
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| 20.5.1 | I'm the Boss: Centralized Control | 247 |
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| 20.5.2 | Work It Out as a Team: Distributed Control | 248 |
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| 20.6 | Architectures for Multi-Robot Control | 249 |
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| 20.6.1 | Pecking Orders: Hierarchies | 250 |
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| 21 | Things Keep Getting Better
Learning | 255 |
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| 21.1 | Reinforcement Learning | 256 |
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| 21.2 | Supervised Learning | 260 |
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| 21.3 | Learning from Imitation/From Demonstration | 261 |
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| 21.4 | Learning and Forgetting | 265 |
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| 22 | Where To Next?
The Future of Robotics | 269 |
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| 22.1 | Space Robotics | 273 |
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| 22.2 | Surgical Robotics | 274 |
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| 22.3 | Self-Reconfigurable Robotics | 276 |
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| 22.4 | Humanoid Robotics | 277 |
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| 22.5 | Social Robotics and Human-Robot Interaction | 278 |
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| 22.6 | Service, Assistive and Rehabilitation Robotics | 280 |
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| 22.7 | Educational Robotics | 283 |
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| 22.8 | Ethical Implications | 285 |
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| Bibliography | 289 |
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| Glossary | 293 |
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| Index | 302 |
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