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Fuzzy logic based design of classical behaviors for mobile robots in ROS middleware

机译:基于模糊逻辑的ROS中间件移动机器人经典行为设计

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Autonomous mobile vehicles are used in many applications to realize special tasks. These tasks involve obstacle avoidance, target reaching and/or tracking. Such vehicles include the use of artificial intelligence to assist the vehicle's operator. Fuzzy logic can be used in the design of an autonomous vehicle to improve the classical control mechanisms. Classical robot control/decision mechanisms can give imperfect results due to sensor compensation errors or calculation costs. These drawbacks can be eliminated by using a combined fuzzy inference. In this study, we have modified the mobile robot ATEKS, which is an intelligent wheelchair, by introducing three fuzzy inference systems to realize goal reaching, obstacle avoidance and a controller for combined behavior selection. Designed fuzzy control system has been implemented on Robot Operating System (ROS) under Ubuntu 12.04 operating system and tested under Gazebo simulation platform. Simulation results verified faithful behavior outputs of ATEKS.
机译:自主移动车辆在许多应用中用于实现特殊任务。这些任务包括避障,目标到达和/或跟踪。这种车辆包括使用人工智能来协助车辆的操作员。模糊逻辑可用于自动驾驶汽车的设计中,以改进经典的控制机制。由于传感器补偿误差或计算成本,传统的机器人控制/决策机制可能会给出不完善的结果。这些缺点可以通过使用组合模糊推理来消除。在这项研究中,我们通过引入三个模糊推理系统来实现目标到达,避障和用于组合行为选择的控制器,从而改进了智能轮椅移动机器人ATEKS。设计的模糊控制系统已在Ubuntu 12.04操作系统下的机器人操作系统(ROS)上实现,并在Gazebo仿真平台上进行了测试。仿真结果验证了ATEKS的忠实行为输出。

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