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Robot Global Path Planning Based on Improved Artificial Fish-Swarm Algorithm

机译:基于改进人工鱼群算法的机器人全局路径规划

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In This study, a new artificial fish-swarm optimization, to improve the foraging behavior of artificial fish swarm algorithm is closer to reality in order to let the fish foraging behavior, increase a look at the link (search) ambient, after examining environment, artificial fish can get more status information of the surrounding environment. Artificial fish screened from the information obtained optimal state for the best direction of movement. Will improve the foraging behavior of artificial fish-swarm algorithm applied to robot global path planning, including the robot to bypass the analog obstacles selected three ways: go obstructions outside, go inside the obstacles, both away obstructions and went outside obstacles Thing achieve robot shortest path planning. Via the MATLAB software emulation test: the improved foraging behavior of artificial fish-swarm algorithm to improve the rapid convergence of the algorithm and stability, improve fish swarm algorithm to the adaptability of the robot global path planning.
机译:在这项研究中,一种新的人工鱼群优化算法,旨在改善人工鱼群算法的觅食行为,使其更接近现实,以便让鱼类觅食行为,增加对环境(环境)的链接(搜索),在检查环境之后,人工鱼可以获得周围环境的更多状态信息。从信息中筛选出的人工鱼获得了最佳运动方向的最佳状态。将改善人工鱼群算法的觅食行为应用于机器人全局路径规划,包括机器人绕过模拟障碍物选择的三种方式:向外部障碍物,向内部障碍物,既远离障碍物又向外部障碍物实现机器人最短路径规划。通过MATLAB软件仿真测试:改进了人工鱼群算法的觅食行为,提高了算法的快速收敛性和稳定性,改进了鱼群算法对机器人全局路径规划的适应性。

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