机器人外文翻译外文文献英文文献采用模糊逻辑控制使自主机器人避障设计.pdf

Autonomous robot obstacle avoidance using a fuzzy logic control

scheme

William Martin

Submitted on December 4, 2009

CS311 - Final Project

1. INTRODUCTION

One of the considerable hurdles to overcome, when trying to describe a

real-world control scheme with first-order logic, is the strong ambiguity found in both

semantics and evaluations. Although one option is to utilize probability theory in

proposed applications for fuzzy logic range from controlling robotic hands with six

degrees of freedom

1

to filtering noise from a digital signal.

2

Due to its easy

implementation, fuzzy logic control has been popular for industrial applications when

advanced differential equations become either computationally 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

2.1. Chassis and sensors

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 2.5 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

electronics, a 9.6 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.

2.3. 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 low-cost 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

3,4,5

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

compute and easy to modify. Keeping computation 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 milliseconds). The distance membership functions allowed the distances from the IR

converted fuzzy values back into crisp values.

3.2.Rule base