## Obstacle Avoidance in Multi-robot Systems: Experiments in Parallel Genetic AlgorithmsObstacle Avoidance in Multi-robot Systems: Experiments in Parallel Genetic Algorithms offers a novel framework for solving the path planning problem for robot manipulators. Simple and efficient solutions are proposed for the path planning problem based on genetic algorithms. One of the attractive features of genetic algorithms is their ability to solve formidable problems in a robust and straightforward manner. Moreover, genetic algorithms are inherently parallel in nature, which makes them ideal candidates for parallel computing implementations.By combining the robustness of genetic algorithms with the power of parallel computers, this book provides an effective and practical approach to solving path planning problems. The book gives details of implementations that allow a better understanding of the complexities involved in the development of parallel path planning algorithms. The material presented is interdisciplinary in nature ? it combines topics from robotics, genetic algorithms, and parallel processing. The book can be used by practitioners and researchers in computer science and engineering. |

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### Contents

Overview | 1 |

Parallel Computing | 8 |

Path Planning | 30 |

Search Techniques | 52 |

Inverse Kinematics | 66 |

Collision Detection | 83 |

Collision Avoidance | 114 |

Examples | 134 |

Discussion Conclusions and Future Work | 155 |

References | 161 |

Parallel Line Proof | 177 |

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### Common terms and phrases

approximate cell decomposition architecture calculations cartesian space Cell tree Chapter collision avoidance algorithm collision detection complex Conference on Robotics configuration space Crossover Data stream dimensions domain end-effector environment Equation error Example fitness function Genetic Algorithms global goal heuristic Hwang and Ahuja IEEE International Conference implemented Instruction stream intersection inverse kinematics problem joint angles joint variables Latombe line segment link parameters machine manipulator moves maximum method MIMD minimum distance motion planning problem multiplexer NP-complete obstacles operation optimal parallel algorithm parallel computing parallel lines parallel processing particle path planner path planning problem performance planar manipulator plane population position potential field processing units real robot real-time systems refer to Figure reference frame rithm robot manipulators Robotics and Automation Sample Section sensors shown in Figure SIMD Simulated annealing SISD slave process solution solve speed speed-up substring suitable Table task planner tion transputers vector velocity workspace Yoshikawa 1990 Zomaya