精选优质文档-倾情为你奉上Fuzzy Logic Based Autonomous Skid Steering Vehicle Navigation L.Doitsidis,K.P.Valavanis,N.C.Tsourveloudis Technical University of Crete Department of Production Engineering and Management Chania,Crete,Greece GR-73100 Idoitsidis ,kimonv,nikostdpem.tuc.grAbstract-A two-layer fuzzy logic controller has been designed for 2-D 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 composed 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 real-time 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. .INTRODUCTION The exist several proposed solutions to the problem of autonomous mobile robot navigation in 2-D uncertain environments that are based on fuzzy logic1,2,evolutionary algorithms 3,as well as methods combining fuzzy logic with genetic algorithms4 and fuzzy logic with electrostatic potential fields5. The paper is the outgrowth of recently published results 9,10,but it studies 2-D environments navigation and collision avoidance of a skid steering vehicle. Skid steering vehicles are compact, light, require few parts to assemble and exhibit agility from point turning to line driving using only the motions, components, 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 straight-line motion. Explicit steering vehicles turn differently since the wheels are moving around two axes. The geometric configuration of a skid steering vehicle in the X-Y plane is shown in Fig1，while at is the heading angle, W is the robot width, the sense of rotation and S1, S2 are the speeds in the either side of the robot. The derived and implemented planner a two-layer fuzzy logic based controller that provides purely” reactive behavior” of the vehicle moving in a 2-D 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.DESIGN OF THE FUZZY LOGIC CONTROL SYSTEM The order to the vehicle movement, a two-layer Madman-type 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 vehicles translational and the rotational speed. Fig. 1.Geometric configuration of the robot in the X-Y plane.A .first layer of the fuzzy logic controller The ATRV-mini is equipped with an array of 24 ultrasonic sensors that are vehicles as shown in Fig.2. 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 Fig.2) 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 overcome 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 Fig.2, 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 manufacturerFig.2. Grouping of the Sensors.The form of each first layer individual fuzzy controller, including the obstacle detection module, is shown in Fig.3.Observing