Developed blimp robot based on ultrasonic sensors using possibilities distribution and fuzzy logic

In this paper, we present obstacles avoidance and altitude control algorithms based on fuzzy sets and possibilities distributions to control the blimp’s complexity and main behaviors of the system.
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Developed blimp robot based on ultrasonic sensors using possibilities distribution and fuzzy logicJournal of Automation and Control Engineering, Vol. 1, No. 2, June 2013Developed Blimp Robot Based On UltrasonicSensors Using Possibilities Distribution andFuzzy LogicRami Al-Jarrah and Hubert RothSiegen University/Automatic Control Engineering, Siegen, GermanyEmail: {rami.al-jarrah, hubert.roth}@uni-siegen.desystems has been applied to control the propulsion andsteering system [5]. However, the tracking system isnever mapped the specified things for airship.The use of solar energy as a renewable source ofpower for such outdoor blimp is also under considerationfor some researches [6]. A few researchers have designedan autonomously controlled indoor blimp and an actionvalue function for motion planning based on the potentialfield method to evaluate the blimp effectiveness in asimulated environment [7]-[8]. The Neural Networkcontrol approach is also used to control the blimp inespecial purposes by collecting the sensor data for theenvironment and implement the multiple rules for thecontrol strategy then the blimp have ability to avoid theobstacles [9]. In fact, it needs more experiments fortraining data to improve the intelligent control. However,most of these researches do not deal with the sensorsbehaviors during the navigation. Therefore, we introducehow it is possible to model these drawbacks by thepossibility distribution (PD) and fuzzy sets. We designthe fuzzy knowledge base experimentally. First, we testthe ultrasonic sensor’s behaviors. Second, we study theeffect of the blimps angle view and the distance betweenthe blimp and the detected objects. Then, the fuzzycontrol takes as input the data provided by the ultrasonicsensors and delivers information for eventual obstacles orinformation about altitude in respect to blimp’s position.In this paper, through an empirical study, the fuzzy setsapproach to control the navigation of blimp robot isexplained in details. The approach is not only applicableto the blimp robot, but also to any other robots.Abstract—In this paper, we present obstacles avoidance andaltitude control algorithms based on fuzzy sets andpossibilities distributions to control the blimp’s complexityand main behaviors of the system. The fuzzy knowledgebase is designed empirically to introduce two-layer fuzzylogic controllers which have the ability to reduce theultrasonic sensor uncertainties and to estimate the shortestdistance between the blimp and the objects. Finally, theresults of the experiments show the algorithm is improvingthe performance of the blimp to avoid obstacles safely andmaintain at a certain altitude.Index Terms—blimp, airship, avoid obstacles, fuzzy control,altitude, UAV robotI.INTRODUCTIONSome of the most difficult applications for robotics arethe unknown environments such as search and rescue,surveillance and environment monitoring. Autonomousnavigation of unmanned vehicles in unstructuredenvironments is a multidiscipline and attractive challengefor researchers. Recently, the unmanned airship becomesfocus interest increasingly because of its advantages suchas long time hovering, much less energy consumed andcost efficiency which made them ideal for exploration ofareas [1]-[2]. However, an important navigation problemis automatic control of altitude and horizontal movement.A second important navigation problem for the blimps isobstacle detection and collision avoidance.In recent years many researchers have developedairships robotic systems and studied the control of theirbehavior. The nonlinear dynamic model of the lowaltitude airship with six degree of freedom is introducedand the flight conditions and the balance between forcesand moments acting on the airship is analyzed [3]. Inorder to develop airships it is important to control thestability. One of the stability theories used is theLyapunov’s theory which analyzes the stability and testthe robustness to verify the controller performance [4].Intelligent control that uses various computingapproaches like neural networks and fuzzy logic is alsoused to control the main behaviors of the blimp. Forexample, the fuzzy logic with soft computing controlII.The flying robot characteristics have some restrictionsconsidering its hardware. Indeed, for any blimp system ifthe envelop volume get higher, the ascending force willincrease and as a result the higher the possible payload.For our blimp system, the goal was to minimize theweight of the needed hardware equipment as soon aspossible and to develop appropriate control algorithms forflying robot which are highly sensitive to outsideinfluences to operate as a fully autonomous robot. Themain components are shown in Fig. 1 which showsManuscript received October 20, 2012; revised December 22, 2012.©2013 Engineering and Technology Publishingdoi: 10.12720/joace.1.2.119-125THE BLIMP SYSTEM119Journal of Automation and Control Engineering, Vol. 1, No. 2, June 2013gondola onboard unit (GOU) with all the electroniccomponents necessary to control the three motors.A. The Main Unit (MU)The processing control unit is the core of the systemand it is distributed among Atmega microcontroller whichhandles stability control and maintaining blimp attitudeset points. Our chosen for this microcontroller depends onits ability to interface with other components in thesystem.B. Inertial Measurement Unit (IMU)As a flying robot the Bryan angles (roll, pitch, and yaw)are required and to obtain these angles an inertialmeasurement unit (IMU) was used. The accelerometerdata along with the gyroscope data about all three axeswill be taken into contexts, allowing the blimp to knowits attitude along with its distance traveled at any point intime.C. Motor DriversThey are necessary to control the speed of each motor.The drivers ...