Industrial Robot ( IF 1.8 ) Pub Date : 2021-09-09 , DOI: 10.1108/ir-05-2021-0091 Abhishek Kumar Kashyap 1 , Dayal R. Parhi 1
Purpose
This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic.
Design/methodology/approach
The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location.
Findings
The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance.
Originality/value
A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.
中文翻译:
复杂工作空间中模糊逻辑控制器辅助猫头鹰搜索算法的仿人机器人避障与路径规划
目的
本文旨在概述并在仿人机器人中实现一种新颖的混合控制器,以绘制最佳路径。混合控制器是使用猫头鹰搜索算法 (OSA) 和模糊逻辑设计的。
设计/方法/方法
最佳转向角 (OS) 用于处理位于工作空间中的障碍物,这是混合 OSA 模糊控制器的输出。它是通过在模糊逻辑中提供 OSA 的输出,即中间转向角 (IS) 获得的。它是通过提供各个方向的障碍物距离和机器人当前位置的目标距离来获得的。
发现
本研究基于人形NAO在复杂工作空间中的导航。因此,在不同复杂工作空间中的 3D 模拟器中执行各种模拟。他们的结果的验证是通过使用建议的控制器在类似工作空间中进行的各种实验来完成的。他们的结果之间的比较表明了可接受的相关性。最终,用另一种现有的导航方法评估所提出的控制器表明性能有显着提高。
原创性/价值
开发了一种新框架来指导复杂工作空间中的人形 NAO,这在现有文献中很少见。仿真和实验工作空间中的检查验证了所设计导航控制器的稳健性。考虑到最小误差范围和接近协作,两个框架的结果在指定的导航变量方面相互评估。最后,关于目前的其他方法,还检查了设计的控制器,并报告了效率的主要修改。