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首页> 外文期刊>Neural network world journal >DEVELOPMENT OF AUTONOMOUS NAVIGATION WHEELCHAIRS BASED ON FUZZY CONTROL
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DEVELOPMENT OF AUTONOMOUS NAVIGATION WHEELCHAIRS BASED ON FUZZY CONTROL

机译:基于模糊控制的自主航行轮椅的研制

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Autonomous robotic wheelchairs based on visual guidance have been devoted to road edge detection. However, the after-detection process, especially the physical interpretation of what had been detected, needs more investigation. There is a wide gap between the scene model based on image processing algorithms and the physical model of the environment where the robotic wheelchair progresses. The aim of this paper is to investigate the interaction between the scene model and the world model; and also a visual control scheme for robot guidance that minimizes the model error induced by processing raw image data is proposed. This solution is developed based on a fuzzy control system, which uses the knowledge base information and the scene model to control the robot motion. On the other hand, the fuzzy control system is finely tuned by feed-backing the mean square errors between the scene model parameters and the knowledge-base data. Finally, the fuzzy controller uses results of these calculations to home the robot on the planned path. This paper also shows the principle of this system and the simulation results confirming the feasibility of the approach.
机译:基于视觉引导的自主机器人轮椅已专门用于道路边缘检测。但是,后检测过程,尤其是对所检测到的物质的物理解释,需要更多的研究。基于图像处理算法的场景模型与机器人轮椅行进环境的物理模型之间存在很大差距。本文的目的是研究场景模型与世界模型之间的相互作用。提出了一种用于机器人制导的视觉控制方案,该方案可将处理原始图像数据引起的模型误差降至最低。该解决方案是基于模糊控制系统开发的,该系统使用知识库信息和场景模型来控制机器人的运动。另一方面,通过反馈场景模型参数和知识库数据之间的均方误差,可以对模糊控制系统进行微调。最后,模糊控制器使用这些计算结果将机器人安置在计划的路径上。本文还展示了该系统的原理和仿真结果,证实了该方法的可行性。

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