【正文】 精的治學(xué)態(tài)度，給我留下了深刻的印象，也使我在大學(xué)學(xué)習(xí)期間受益匪淺。感謝機(jī)械系所有老師大學(xué)期間在我學(xué)習(xí)及工作方面的關(guān)心與幫助。他們對我的悉心指導(dǎo)和無微不至的關(guān)懷將使我終身難忘?！「兄x我所有的同學(xué)，一直以來大家共同努力、共同進(jìn)步，遇到困難互相幫助、互相勉勵(lì)，一齊前進(jìn)。 感謝所有幫助過我，和我所結(jié)識(shí)的所有的人們，是你們一起成就著我的人生。 特別感謝我的父母，感謝他們一直為我操勞，感謝他們一直以來對我的關(guān)心與鼓勵(lì)。外文翻譯：Autonomous robot obstacle avoidance using a fuzzy logic control schemeWilliam MartinSubmitted on December 4, 2009CS311 Final Project1. INTRODUCTIONOne of the considerable hurdles to overe, when trying to describe a realworld control scheme with firstorder logic, is the strong ambiguity found in both semantics and evaluations. Although one option is to utilize probability theory in order to e up with a more realistic model, this still relies on obtaining information about an agent39。s environment with some amount of precision. However, fuzzy logic allows an agent to exploit inexactness in its collected data by allowing for a level of tolerance. This can be especially important when high precision or accuracy in a measurement is quite costly. For example, ultrasonic and infrared range sensors allow for fast and cost effective distance measurements with varying uncertainty. The proposed applications for fuzzy logic range from controlling robotic hands with six degrees of freedom1 to filtering noise from a digital Due to its easy implementation, fuzzy logic control has been popular for industrial applications when advanced differential equations bee either putationally expensive or offer 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 sensorsThe robotic vehicle39。s chassis was constructed from an Excalibur EIMSD2003 remote control toy tank. The device was stripped of all electronics, gears, and extraneous parts in order to work with just the empty case and two DC motors for the tank treads. However, this left a somewhat uneven surface to work on, so highdensity polyethylene (HDPE) rods were used to fill in empty spaces. Since HDPE has a rather low surface energy, which is not ideal for bonding with other materials, a propane torch was used to raise surface temperature and improve bonding with an epoxy adhesive. Three Sharp GP2D12 infrared sensors, which have a range of 10 to 80 cm, were used for distance measurements. In order to mount these appropriately, a by 15 cm piece of aluminum was bent into three even pieces at 135 degree angles. This allows for the IR sensors to take three different measurements at 45 degree angles (right, middle, and left distances). This sensor mount was then attached to an HDPE rod with mounting tape and the rod was glued to the tank base with epoxy. Since the minimum distance that can be reliably measured with these sensors is 10 cm, the sensors were placed about 9 cm from the front of the vehicle. This allowed measurements to be taken very close to the front of the robot.. ElectronicsIn order to control the speed of each motor, pulsewidth modulation (PWM) was used to drive two L2722 op amps in open loop mode (Fig. 1). The high input resistance of these ICs allow for the motors to be powered with very little power draw from the PWM circuitry. In order to isolate the motor39。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.. MicrocontrollerComputation 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 FORIn 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 functionsThree 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. baseOnce 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 motor speeds.(a)(b)(c)Fig. 2. The membership functions used for (a) distance, (b) translation speed, and (c) rotational speed. These functions were adapted from similar work done in reference 3.4. RESULTSThe fuzzy control scheme allowed for the robot to quickly respond to obstacles it could detect in its environment. This allowed it to follow walls and bend around corners decently without hitting any