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Intelligent Planning for Mobile Robotics: Algorithmic Approaches [Kietas viršelis]

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Intelligent Planning for Mobile Robotics: Algorithmic Approaches presents content coverage on the basics of artificial intelligence, search problems, and soft computing approaches. This collection of research provides insight on both robotics and basic algorithms and could serve as a reference book for courses related to robotics, special topics in AI, planning, applied soft computing, applied AI, and applied evolutionary computing. It is an ideal choice for research students, scholars, and professors alike.
Preface vi
Acknowledgment xi
Chapter 1 Introduction
1(25)
Introduction
1(1)
Preliminaries and Definitions
2(4)
Applications
6(4)
Problem Solving in Robotics
10(10)
Robot Motion Planning
20(2)
Conclusion
22(4)
Chapter 2 Graph Based Path Planning
26(28)
Introduction
26(1)
Graphs
27(4)
Robot Motion Planning
31(5)
Graph Search
36(2)
State Space Approach
38(1)
Breadth First Search
39(2)
Depth First Search
41(2)
A* Algorithm
43(4)
Multi-Neuron Heuristic Search
47(2)
D* Algorithm
49(2)
Experimental Results
51(1)
Conclusion
51(3)
Chapter 3 Common Planning Techniques
54(23)
Introduction
54(2)
Planning Using Bellman Ford Algorithm
56(4)
Rapidly Exploring Random Trees
60(7)
Embedded Sensor Planning
67(4)
More Planning Techniques
71(2)
Conclusion
73(4)
Chapter 4 Evolutionary Robotics I
77(23)
Introduction
77(2)
Principles of Genetic Algoirhtm
79(2)
Simple Genetic Algorithm
81(7)
Convergence in Genetic Algorithm
88(1)
Path Planning by Genetic Algorithm
89(3)
Curve Smoothening
92(2)
Evolutionary Strategies
94(3)
Conclusion
97(3)
Chapter 5 Evolutionary Robotics 2
100(26)
Introduction
100(2)
Swarm Intelligence
102(7)
Swarm Intelligence in Path Planning
109(1)
Genetic Programming
110(6)
Grammatical Evolution
116(5)
Path Planning by Genetic Programming
121(1)
Conclusion
122(4)
Chapter 6 Behavioral Path Planning
126(28)
Introduction
126(2)
Fuzzy Concepts
128(2)
Fuzzy Operators
130(4)
Fuzzy Inference Systems
134(3)
Motion Planning by Fuzzy Inference System
137(2)
Neural Networks
139(6)
Path Planning Using Neural Networks
145(4)
Path Planning Using Neural Dynamics
149(1)
Conclusion
150(4)
Chapter 7 Hybrid Graph-Based Methods
154(37)
Introduction
155(5)
Hierarchical MNHS Algorithm
160(7)
The Hierarchical Approach
167(3)
Probability-Based Fitness
170(1)
Simulation and Results
171(7)
Hybrid MNHS and Evolutionary Algorithms
178(6)
Relation Between EA and MNHS
184(2)
Results
186(1)
Conclusion
187(4)
Chapter 8 Hybrid Evolutionary Methods
191(39)
Introduction
192(2)
Evolutionary Algorithm with Momentum
194(4)
Momentum
198(2)
Evolutionary Algorithm
200(1)
Variable Genetic Parameters
201(1)
Results
202(7)
HGAPSO with Momentum
209(4)
Results
213(2)
Hierarchical Evolutionary Algorithm: Hierarchy 1
215(5)
Hierarchical Evolutionary Algorithm: Hierarchy 2
220(2)
Results
222(4)
Conclusion
226(4)
Chapter 9 Hybrid Evolutionary Methods
230(29)
Introduction
231(3)
Fusion of A* and Fuzzy Planner
234(7)
Simulation and Results
241(9)
Evolving Robotic Path
250(2)
Simulations and Results
252(1)
Planning by Accelerated Nodes
253(3)
Conclusion
256(3)
Chapter 10 Multi-Robot Systems
259(23)
Introduction
260(1)
Planning Problems in Robotics
260(4)
Multi-Robot Systems
264(3)
Problems in Multi-Robotic Systems
267(7)
Multi Robot Path Planning
274(2)
Approaches
276(2)
Conclusion
278(4)
Chapter 11 Conclusion
282(7)
Concluding Remarks
282(7)
Compilation of References 289(14)
About the Authors 303(1)
Index 304