Neural Preprocessing and Control of Reactive Walking Machines: Towards Versatile Artificial Perception-Action Systems

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Springer Science & Business Media, May 10, 2007 - Technology & Engineering - 187 pages
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Biologically inspired walking machines are fascinating objects to study, from thepointofviewoftheirmechatronicaldesignaswellastherealisationofc- trol concepts. Research on this subject takes its place in a rapidly growing, highly interdisciplinary ?eld, uniting contributions from areas as diverse as biology, biomechanics, material science, neuroscience, engineering, and c- puter science. Nature has found fascinating solutions for the problem of legged loco- tion, and the mechanisms generating the complex motion patterns performed by animals are still not very well understood. Natural movements provide the impressionofeleganceandsmoothness,whereastheimitationoftheirarti?cial analogues still looks rather clumsy. The diverse research on arti?cial legged locomotion mainly concentrated on the mechanical design and on pure movement control of these machines; i.e., in general these machines were unable to perceive their environment and reactappropriately.Contributionsdevelopingembodiedcontroltechniquesfor sensor-driven behaviors are rare, and if considered, they deal only with one type of behavior; naturally, this is most often an obstacle avoidance behavior. There are only a few approaches devoted to the multimodal generation of several reactive behaviors. This book presents a pioneering approach to tackle this challenging pr- lem. Inspired by the obstacle avoidance and escape behaviors of cockroaches and scorpions, which here are understood as negative tropisms, and by the prey-capturing behavior of spiders, taken as a positive tropism, corresponding sensors and neural control modules are introduced in such a way that wa- ing machines can sense and react to environmental stimuli in an animal-like fashion.
 

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Contents

Introduction
1
12 Aims and Objectives
8
13 Organization of the Book
11
Biologically Inspired PerceptionAction Systems
13
211 Obstacle Avoidance Behavior
15
212 Prey Capture Behavior
19
22 Morphologies of Walking Animals
22
221 A Salamander
23
512 Preprocessing of a Tactile Signal
82
513 Preprocessing of Antennalike Sensor Data
87
52 Neural Control of Walking Machines
89
522 The Velocity Regulating Network
94
523 The Modular Neural Controller
98
53 Behavior Control
99
532 The Sound Tropism Controller
103
533 The Behavior Fusion Controller
104

222 A Cockroach
24
23 Locomotion Control of Walking Animals
26
24 Conclusion
29
Neural Concepts and Modeling
30
311 A Biological Neuron
32
312 An Artificial Neuron
33
313 Models of Artificial Neural Networks
36
32 Discrete Dynamics of the Single Neuron
37
33 Evolutionary Algorithm
41
34 Conclusion
45
Physical Sensors and Walking Machine Platforms
47
412 A Stereo Auditory Sensor
49
413 Antennalike Sensors
51
42 Walking Machine Platforms
55
421 The FourLegged Walking Machine AMOSWD02
57
422 The SixLegged Walking Machine AMOSWD06
61
43 Conclusion
64
Artificial PerceptionAction Systems
66
511 Auditory Signal Processing
68
54 Conclusion
111
Performance of Artificial PerceptionAction Systems
113
612 The Stereo Auditory Sensor Data
118
613 The Antennalike Sensor Data
123
62 Implementation on the Walking Machines
126
621 Obstacle Avoidance Behavior
128
622 Sound Tropism
135
623 Behavior Fusion
142
63 Conclusion
145
Conclusions
146
72 Possible Future Work
149
Description of the Reactive Walking Machines
151
A2 The AMOSWD06
152
A3 Mechanical Drawings of Servomotor Modules and the Walking Machines
154
Symbols and Acronyms
165
References
167
Index
183
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