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首页> 外文期刊>International Journal of Innovative Computing Information and Control >AUTO-GENERATION MODEL/CONTROL FUZZY IMAGE MOBILE ROBOT SYSTEMS DESIGN
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AUTO-GENERATION MODEL/CONTROL FUZZY IMAGE MOBILE ROBOT SYSTEMS DESIGN

机译：自动生成模型/控制模糊图像移动机器人系统设计

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摘要

The evolutional particle swarm optimization (PSO) learning algorithm with self-regulated parameters and an auto-configured fuzzy model machine were applied to efficiently generate the mobile robot control systems. The omnidirectional image sensor was mounted on the mobile robot platform to capture objects surrounding the mobile robot with smart image processing technology to approach the desired traveling path. The generated kinematics mobile robot represents the behavior of the mobile robot in the visual traveling space. The appropriate fuzzy control rules of a mobile robot can be automatically extracted by the direction of the flexibly defined fitness function. The proposed self-learning algorithm can simultaneously avoid obstacles, approach the shortest path, and select the required fuzzy rules numbers. Based on the parameters of the self-generation procedure, the appropriate fuzzy rules were derived to guide the mobile robot toward the desired targets as soon as possible. Six examples of nonlinear mobile robot control problems were applied to demonstrate the adaptability of the self-generated learning algorithm. In the simulated examples, several blocks of various sizes (20, 30, and 40), various locations, and unusual initial and targeted positions were considered to test the adaptation of the learning scheme. Two types of evolutionary PSO learning algorithms were applied to achieve the desired results: one algorithm generates fuzzy rules with an adaptive procedure, and the other algorithm generates fuzzy rules with a random scheme. A comparison of the simulation results of the adaptive PSO (APSO) and random PSO (RPSO) learning algorithms showed that the appropriate mobile robot fuzzy systems were automatically generated by the APSO to form the required fuzzy rules, and detect and escape the obstacles within the desired and shorter traveling path when the initial environments changed.
机译：应用具有自调节参数的进化粒子群优化（PSO）学习算法和自动配置的模糊模型机来有效地生成移动机器人控制系统。全向图像传感器安装在移动机器人平台上，通过智能图像处理技术捕获移动机器人周围的物体，以接近所需的行进路径。生成的运动学移动机器人表示该移动机器人在视觉旅行空间中的行为。可以通过灵活定义的适应度函数的方向自动提取移动机器人的适当模糊控制规则。所提出的自学习算法可以同时避开障碍物，走近最短路径并选择所需的模糊规则数。基于自生成过程的参数，导出了适当的模糊规则，以将移动机器人尽快引导至所需目标。应用六个非线性移动机器人控制问题的例子来证明自生学习算法的适应性。在模拟示例中，考虑了各种大小（20、30和40），各种位置以及不正常的初始位置和目标位置的几个块，以测试学习方案的适应性。应用了两种类型的进化PSO学习算法来获得期望的结果：一种算法通过自适应过程生成模糊规则，另一种算法通过随机方案生成模糊规则。自适应PSO（APSO）和随机PSO（RPSO）学习算法的仿真结果比较表明，APSO自动生成了适当的移动机器人模糊系统，以形成所需的模糊规则，并检测并逃避了障碍物。初始环境变化时所需的行驶路径和较短的行驶路径。

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