Genetic algorithms for autonomous robot navigation
Genetic algorithms for autonomous robot navigation
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DOI：
10.1109/mim.2007.4428579
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发表时间：
2007-12
影响因子：
2.1
通讯作者：
T. Manikas;K. Ashenayi;R. Wainwright
中科院分区：
文献类型：
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作者：
T. Manikas;K. Ashenayi;R. Wainwright
工程师和科学家使用仪器和测量设备来获取特定环境的信息，如温度和压力。这项任务可以使用便携式仪表手动执行。然而，在许多情况下，当从远程站点或从潜在的有害环境收集数据时，这种方法可能是不切实际的。在这些应用中，自主导航方法允许移动机器人独立于人类的存在或干预来探索环境。移动机器人包含测量设备并记录数据，然后将其传输或带回操作员。机器人探测导航环境中的障碍物需要传感器，机器人需要机器智能来规划绕过这些障碍物的路径。遗传算法的使用是机器智能应用于现代机器人导航的一个例子。遗传算法是一种启发式优化方法，其机制类似于生物进化。本文提供了对移动机器人自主导航的初步了解，描述了用于检测障碍物的传感器，并描述了用于路径规划的遗传算法。
Engineers and scientists use instrumentation and measurement equipment to obtain information for specific environments, such as temperature and pressure. This task can be performed manually using portable gauges. However, there are many instances in which this approach may be impractical; when gathering data from remote sites or from potentially hostile environments. In these applications, autonomous navigation methods allow a mobile robot to explore an environment independent of human presence or intervention. The mobile robot contains the measurement device and records the data then either transmits it or brings it back to the operator. Sensors are required for the robot to detect obstacles in the navigation environment, and machine intelligence is required for the robot to plan a path around these obstacles. The use of genetic algorithms is an example of machine intelligence applications to modern robot navigation. Genetic algorithms are heuristic optimization methods, which have mechanisms analogous to biological evolution. This article provides initial insight of autonomous navigation for mobile robots, a description of the sensors used to detect obstacles and a description of the genetic algorithms used for path planning.