【正文】 1999,(5). [18]孟立凡,鄭賓,候文 .:,(2).附錄A：外文文獻原文Fuzzy Logic Based Autonomous Skid Steering Vehicle Navigation , Technical University of Crete Department of Production Engineering and Management Chania,Crete,Greece GR73100 {Idoitsidis ,kimonv,nikost}AbstractA twolayer fuzzy logic controller has been designed for 2D autonomous Navigation of a skid steering vehicle in an obstacle filled environment. The first layer of the Fuzzy controller provides a model for multiple sonar sensor input fusion and it is posed of four individual controllers, each calculating a collision possibility in front, back, left and right directions of movement. The second layer consists of the main controller that performs realtime collision avoidance while calculating the updated course to be applicability and implementation is demonstrated through experimental results and case studies performed o a real mobile robot.Keywords Skid steering, mobile robots, fuzzy navigation. Ⅰ .INTRODUCTION The exist several proposed solutions to the problem of autonomous mobile robot navigation in 2D uncertain environments that are based on fuzzy logic[1],[2],evolutionary algorithms [3],as well as methods bining fuzzy logic with genetic algorithms[4] and fuzzy logic with electrostatic potential fields[5]. The paper is the outgrowth of recently published results [9],[10],but it studies 2D environments navigation and collision avoidance of a skid steering vehicle. Skid steering vehicles are pact, light, require few parts to assemble and exhibit agility from point turning to line driving using only the motions, ponents, and swept volume needed for straight line driving. Skid steering vehicle motion differs from explicit steering vehicle motion in the way the skid steering vehicle turns. The wheels rotation is limited around one axis and the back of steering wheel results in navigation determined by the speed change in either side of the skid steering vehicle. Same speed in either side results in a straightline motion. Explicit steering vehicles turn differently since the wheels are moving around two axes. The geometric configuration of a skid steering vehicle in the XY plane is shown in Fig1，while at is the heading angle, W is the robot width, θ the sense of rotation and S1, S2 are the speeds in the either side of the robot. The derived and implemented planner a twolayer fuzzy logic based controller that provides purely” reactive behavior” of the vehicle moving in a 2D obstacle filled environment, with inputs readings from a ring of 24 sonar sensors and angle errors, and outputs the updated rotational and translational velocities of the vehicle.Ⅱ.DESIGN OF THE FUZZY LOGIC CONTROL SYSTEM The order to the vehicle movement, a twolayer Madmantype controller has been designed and implemented. In the first layer, there are four fuzzy logic controllers repondible for obstacle detection and calculation of the collision possibleilities in the four main directions, front, back, left and right. The possibilities calculated in the first layer are the input to the second layer along with the angle error (the difference between the robot heading angle and the desired target angle), and the output is the updated vehicle’s translational and the rotational speed. Fig. configuration of the robot in the XY plane. A .first layer of the fuzzy logic controller The ATRVmini is equipped with an array of 24 ultrasonic sensors that are vehicles as shown in . The ultrasonic sensors that are used are manufactured by Polaroid.After experiment with, and testing several methods concerning sonar sensor date grouping and management, it was first decided to follow the sensor grouping in pairs as proposed in [8](considering the ATRV –mini twelve sonar group Ais=1,…..,12, have been enumerated as shown in ) and then divide the sun of the provided pair sensor data by two to determine the distance from the (potential) obstacle. However, this method gave unsatisfactory results due to the ATRV –minis specific sensor unreliability. Even in cases with obstacles present in the vicinity of the vehicle, the sensors were detecting a “free path”. To overe this problem, a modified, simpler, sensor grouping and data management method was tested that return much better and accurate results: The sensors were again grouped in pairs according to , but the minimum of the (potential) obstacle. Each ATRV –mini sonar returns from obstacles at a maximum distance of 4metres (experimentally verified as opposed to different value provided by the sonar sensors manufacturer. Grouping of the Sensors.The form of each first layer individual fuzzy controller, including the obstacle detection module, is shown in , data from group sensors A1, A2, ….,A5(5 inputs) and group sensors A7, A8 , …,A11(5 inputs) serve as inputs to the individual controllers responsible for the calculation of the front and back collision possibilities, respectively. Data from group sensors A5, A6, A7 (3 inputs) serve as input to calculate the left and right possibilities, respectively. The individual fuzzy controllers utilize the same membership functions to calculate the collision possibilities. The linguistic values of the variable distance_from_obstance are defined to be three, near, meium_distance, away with membership functions as shown in reflecting the maximum distance of 4 meters a sonar returns accurate information about potential obstacles. detection module. Variable Distance_ From _ Obstacle.The first layer output is a collision possibility in each direction taking values from 0 to linguistic variables describing each direction output variable collision possibility (with empiricallyDerived for best performance) membership functions as show