機(jī)器人外文翻譯外文文獻(xiàn)英文文獻(xiàn)采用模糊邏輯控制使自主機(jī)器人避障設(shè)計(jì)-展示頁
2024-11-14 04:46本頁面
【正文】 fer no known solution. This project is an attempt to take advantage of these fuzzy logic simplifications in order to implement simple obstacle avoidance for a mobile robot. 2. PHYSICAL ROBOT IMPLEMENTATION . Chassis and sensors The robotic vehicle39。s power supply from the rest of the electronics, a V NiCad battery was used separately from a standard 9 V that demand on the op amps led to a small amount of overheating during continuous operation. This was remedied by adding small heat sinks and a fan to the forcibly disperse heat. Fig. 1. The control circuit used for driving each DC motor. Note that the PWM signal was between 0 and 5 V. . Microcontroller Computation was handled by an Arduino Duemilanove board with an ATmega328 microcontroller. The board has low power requirements and modifications. In addition, it has a large number of prototyping of the control circuit and based on the Wiring language. This board provided an easy and lowcost platform to build the robot around. 3. FUZZY CONTROL SCHEME FOR In order to apply fuzzy logic to the robot to interpret measured distances. While the final algorithm depended critically on the geometry of the robot itself and how it operates, some basic guidelines were followed. Similar research projects provided both simulation results and ideas for implementing fuzzy ,4,5 . Membership functions Three sets of membership functions were created to express degrees of membership for distances, translational speeds, and rotational speeds. This made for a total of two input membership functions and eight output membership functions (Fig. 2). Triangle and trapezoidal functions were used exclusively since they are quick to pute and easy to modify. Keeping putation time to a minimum was essential so that many sets of data could be analyzed every second (approximately one every 40 milliseconds). The distance membership functions allowed the distances from the IR sensors to be quickly fuzzified, while the eight speed membership functions converted fuzzy values back into crisp values. base Once the input data was fuzzified, the eight defined fuzzy logic rules (Table I) were executed in order to assign fuzzy values for translational speed and rotation. This resulted in multiple values for the each of the fuzzy output ponents. It was then necessary to take the maximum of these values as the fuzzy value for each ponent. Finally, these fuzzy output values were defuzzified using the maxproduct technique and the result was used to update each of the mot
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