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Smart navigation of autonomous mobile robots by Time-Variant Artificial Potential Fields: A fuzzy approach

机译:自动移动机器人的智能导航通过时间变量的人工潜在田地:模糊方法

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Several recently-designed schemes for the navigation problem, which lead to impressive denouements for custom mobile robots, could not be considered as efficient approaches for power-optimized navigation of mobile robots in planetary missions. Furthermore, with due attention to noticeable complexity of our previous established strategy, i.e. Time-Variant Artificial Potential Fields (TAPE), a novel smart control approach is presented in this paper. Devised central fuzzy controller syncretizes both remarkable flexibility in navigation of the rover and coherent control without switching among different control methods in presence of the intrinsic complexity of TAPFs. This objective is also acquired by using various sensors with trivial computational overhead instead of utilization of machine vision techniques; Whereas, the vehicle will be encountered with data consolidation for some different sensors in order to reach into an unified perception from its circumferential environment. Simulated results and practical experiments, which are obtained through PIONEER™ mobile robot as the test bed, verify our accomplishments for TAPF handling and catch an efficient result for navigation of space rovers.
机译:几个用于导航问题的最近设计的计划,这导致了对定制移动机器人的令人印象深刻的结局,不能被视为行星任务中移动机器人电力优化导航的有效方法。此外,由于我们以前的稳定策略的明显复杂性,即时间变体人工势域(胶带),本文提出了一种新颖的智能控制方法。设计的中央模糊控制器在罗珀和相干控制的导航中同时进行了显着的灵活性,而不在存在于基因组的内在复杂性存在下不同的控制方法之间的切换。还通过使用具有琐碎的计算开销而不是利用机器视觉技术的各种传感器来获取该目的;然而,对于一些不同的传感器,将遇到车辆的数据整合,以便达到统一的圆周环境的统一感知。通过Pioneer™移动机器人作为测试床获得的模拟结果和实际实验,验证了我们对TapF处理的成就,并捕获了空间群体导航的有效结果。

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