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Cognitive Map approach for mobility path optimization using multiple objectives genetic algorithm

机译:使用多目标遗传算法的移动路径优化的认知地图方法

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This paper describes the evolutionary planning strategies for mobile robot to move along the streamlined collision-free paths in a known static environment. The Cognitive Map method is combined with genetic algorithm to derive the mobile robot optimal moving path towards its goal functions. In this study, multi-objectives genetic algorithm (MOGA) is utilized due to there are more than one objective need to be achieved while planning for the robot moving path. Goal-factor and obstacle-factor are the key parameters incorporated in the MOGA fitness functions. The simulation results showed that the hybrid Cognitive Map approach with MOGA is capable of navigating a robot situated among non-moving obstacles. The proposed hybrid method demonstrates good performance in planning and optimizing mobile robot moving path with stationary obstacles and goal.
机译:本文介绍了移动机器人的进化规划策略,以沿着已知的静态环境中的简化的碰撞路径移动。认知地图方法与遗传算法相结合,从而导出了朝向其目标功能的移动机器人最佳移动路径。在该研究中,由于需要在计划机器人移动路径的同时需要实现多目标遗传算法(MOGA),因此需要实现多于一个目标。目标因素和障碍因子是在MOGA健身功能中包含的关键参数。仿真结果表明,具有MOGA的混合认知地图方法能够导航位于非移动障碍物中的机器人。所提出的混合方法在规划和优化具有固定障碍物和目标的移动机器人移动路径方面表现出良好的性能。

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