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Implementation of grey wolf optimization controller for multiple humanoid navigation
Computer Animation and Virtual Worlds ( IF 0.9 ) Pub Date : 2020-03-05 , DOI: 10.1002/cav.1919 Manoj K. Muni 1 , Dayal R. Parhi 1 , Priyadarshi Biplab Kumar 2
Computer Animation and Virtual Worlds ( IF 0.9 ) Pub Date : 2020-03-05 , DOI: 10.1002/cav.1919 Manoj K. Muni 1 , Dayal R. Parhi 1 , Priyadarshi Biplab Kumar 2
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In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision‐free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri‐net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids.
中文翻译：
多类人形导航灰狼优化控制器的实现
在本文中，灰狼优化控制器（GWOC）被认为是一种用于多类人形导航的多目标技术。GWOC 激活后，类人机器人会模仿灰狼的群体狩猎行为，并在存在静态和动态障碍的情况下以无碰撞方式向目标导航。狼群中的狼要么发散寻找猎物，要么聚集在一起，跟随最佳搜索代理（首领阿尔法）攻击猎物。GWOC 有能力让类人机器人免于陷入局部最小值，同时有助于它朝着全局最小值前进。与其他智能技术相比，GWOC 提供了更好的结果，因为它具有安全边界、保护、跟随、狩猎和关怀五个特性。在实验室条件下对单个和多个人形 NAO 进行了模拟和实验导航。从仿真结果和实验数据可以证实，GWOC为人形机器人在具有不同形状障碍物的复杂环境中提供了全局最小值。在导航多个类人机器人时考虑使用 Petri-net 控制器，因为在多个类人机器人导航期间，一个类人机器人充当其他类人机器人的动态障碍。
更新日期：2020-03-05
中文翻译：
多类人形导航灰狼优化控制器的实现
在本文中，灰狼优化控制器（GWOC）被认为是一种用于多类人形导航的多目标技术。GWOC 激活后，类人机器人会模仿灰狼的群体狩猎行为，并在存在静态和动态障碍的情况下以无碰撞方式向目标导航。狼群中的狼要么发散寻找猎物，要么聚集在一起，跟随最佳搜索代理（首领阿尔法）攻击猎物。GWOC 有能力让类人机器人免于陷入局部最小值，同时有助于它朝着全局最小值前进。与其他智能技术相比，GWOC 提供了更好的结果，因为它具有安全边界、保护、跟随、狩猎和关怀五个特性。在实验室条件下对单个和多个人形 NAO 进行了模拟和实验导航。从仿真结果和实验数据可以证实，GWOC为人形机器人在具有不同形状障碍物的复杂环境中提供了全局最小值。在导航多个类人机器人时考虑使用 Petri-net 控制器，因为在多个类人机器人导航期间，一个类人机器人充当其他类人机器人的动态障碍。
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