Robot navigation in very cluttered environments by preference-based fuzzy behaviors
Robot navigation in very cluttered environments by preference-based fuzzy behaviors
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DOI：
10.1016/j.robot.2007.07.006
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发表时间：
2008-03-31
影响因子：
4.3
通讯作者：
Collins, Emmanuel G., Jr.
中科院分区：
文献类型：
--
作者：
Selekwa, Majura F.;Dunlap, Darnion D.;Collins, Emmanuel G., Jr.
自主地面车辆（AGV）应用的关键挑战之一是在密集障碍物的环境中导航。控制任务变得更加复杂时，障碍物的配置是不知道的先验。这种系统最流行的控制方法是基于反应式本地导航方案，该方案将机器人动作与传感器信息紧密耦合。由于环境的不确定性，人们提出了模糊行为系统。在应用模糊反应行为为基础的导航控制系统中最困难的问题是仲裁或融合的反应的个人行为，这是解决这里使用的偏好逻辑。本文提出了一种基于偏好的模糊行为系统的设计，用于机器人车辆的导航控制，使用多值逻辑框架。仿真和实验结果表明，所提出的方法可以使机器人顺利，有效地通过杂乱的环境，如茂密的森林导航。与矢量场直方图方法（VFH）的实验比较表明，该方法通常产生更平滑，虽然更长的路径到目标。由爱思唯尔公司出版
One of the key challenges in application of Autonomous Ground Vehicles (AGVs) is navigation in environments that are densely cluttered with obstacles. The control task becomes more complex when the configuration of obstacles is not known a priori. The most popular control methods for such systems are based on reactive local navigation schemes that tightly couple the robot actions to the sensor information. Because of the environmental uncertainties, fuzzy behavior systems have been proposed. The most difficult problem in applying fuzzy-reactive-behavior-based navigation control systems is that of arbitrating or fusing the reactions of the individual behaviors, which is addressed here by the use of preference logic. This paper presents the design of a preference-based fuzzy behavior system for navigation control of robotic vehicles using the multivalued logic framework. As shown in simulation and experimental results, the proposed method allows the robot to smoothly and effectively navigate through cluttered environments such as dense forests. Experimental comparisons with the vector field histogram method (VFH) show that the proposed method usually produces smoother albeit longer paths to the goal. Published by Elsevier B.V.