Development of Parallel Parking System

Development of line of latitude Parking SystemExecutive SummaryIntroduction/BackgroundTraditionally kick downstairsment of micro appendageor ground bear systems involved the following(a) steps. The date algorithmic ruleic programic rule was designed by a systems or a control engineer. The algorithm would then be coded in scheduling languages like assembly, C or C++ which would be employ on a microprocessor origind hardw be the restraint.The problem with this method was the verification of the algorithm would be possible only once the whole system was developed. Any errors in design would prove extremely costly. Also in converting control system of logic specification into software in that location is a high probability of translation errors that would lead to err nonpareilous results.m present creationd Design is a software methodology which addresses these issues and is gaining a lot of popularity currently in the industry.The scope of this project is to apply the influence Based Design methodology to develop a control algorithm that eases the double laying of a car problem for a novice driver.Aims and ObjectivesThe aim of this project is to develop a check park system use Model Based Design. The objectives are to understand the parallel park problem and methods to solve it, steps involved in Model Based Design and explore the accident of carrying out of a parallel parking algorithm on a Lego Mindstorms Robotics Kit.AcheivementsA fuzzy logic based parallel parking algorithm was advantagefully simulated. Model Based Design concepts were explored and implemented in the design. Programs developed in Simulink were prosperously tested on the Lego Mindstorms KIT using Real Time shop Embedded Coder, EC Robot Toolbox, nxtOSEK and other associated tools. finiss/RecommendationsThe parallel parking strategy implemented did show good results. However, real life scenarios may be diametrical than the iodin discussed. For example, the controller d epends on wall following. This implies there has to be 3 sides closed in a gap for the parking controller to work.The development of the algorithm was with the intent of being able to execute it on a Lego Mindstorms Kit. However, there were some issues regarding this. The main being lack of a afloat(p) channelize social unit on the NXT Brick. Though it is possible to execute floating point programs on the NXT, it causes severe performance issues.The controller developed use the fuzzy logic cylinder blockset which inherently uses a lot of floating point variables. adept method to overcome this draw back could be growth a fixed point mutant of the fuzzy logic blockset.Other parking strategies involving neoclassical control could be experimented with Lego Mindstorms. Complex tasks like travel guidebook planning could be executed on a computer communicating with the NXT via Bluetooth and the control tasks could be implemented on the NXT using fixed point math.IntroductionProble m DescriptionThe parallel parking period of play of a car has been a topic of academic and industry interest. The car or a car-like mobile robot hence referred to as CLMR has inherently a constraint known as a nonholonomic constraint. This is problem can be summarised as follows (Pushkin Kachroo and Patricia Mellodge, 2004.). If a system has restrictions on its velocity, but those restrictions does not cause restrictions in its inclineing the systems is said to be nonholonomically constrained. Viewed in some other way, the systems local movement is restricted, but its global movement is not.Mathematically, this means that the velocity constraints cannot be integrated. For e.g. during a parallel parking manoeuvre, when a driver arrives next to a parking space, he cannot simply slide his car sideways into the spot. The car is not capable of slew sideways and this is the velocity restriction. However by moving the car backward and forward and turning the wheels the car can be moved into the parking space. Ignoring the restrictions ca apply by the external objects the car can be located at any position with any orientation, despite the lack of sideways movement (Pushkin Kachroo and Patricia Mellodge, 2004.).The challenge in this project is to not only address the parallel parking problem but also to develop it using advanced software design techniques involving Model Based DesignLiterature SurveyThe parallel parking problem can be viewed as a subtask of robot motion planning and control. on that point is has been considerable research in this area. De Luca et al (1998) classifies all robot motion tasks into 3 subtasks as follows.Point-to-point motion The robot must happen a craved goal flesh starting from a given initial configuration.Path following The robot must reach and follow a geometric path in the Cartesian space starting from a given initial configuration (on or off the path).Trajectory tracking The robot must reach and follow a trajectory in the Ca rtesian space (i.e., a geometric path with an associated timing law) starting from a given initial configuration (on or off the trajectory)A parallel parking problem can then considered as a point to point motion task or a path following task and a feedback control law if proposed for the same.(Dongbing and Huosheng 2000) thrust developed more advanced control strategies involving Generalized Predictive comprise and Neural Networks based predictive control.Fuzzy Logic based controllers have also been used to solve the parallel parking problem. Shih-Jie and Tzuu-Hseng (2002) proposes a rule base based on the appearer space of the CLMR edges to the parking spot.Kuang-Yow et al (1999) proposes a fuzzy sliding mode controller to solve the parallel parking problem.Holve and Protzel (1996) suggest another fuzzy logic based controller and a parking algorithm based on human experience. It involves finding an appropriate space for parking, stopping at the right place, executing the parki ng procedure and to do so without colliding with any obstacles.SummaryThe classical control methods discussed above are generally more interlocking compared to other ones. Most of them rely on trajectory generation and then a control algorithm for trajectory tracking or path following. The success of the algorithm in a real time implementation also depends upon the accuracy of the positioning systems which increases the complexity and the hardware cost of this method.While the fuzzy logic based controllers are more intuitive and easier to design the performance is suboptimal compared to the classical controllers. But they are more robust to sensor uncertainties (Holve, R. Protzel, P. 1996).The goal of this project is to develop a parallel parking algorithm suitable for implementation on a Lego Mindstorms robotics kit. Being able to solve the parallel parking problem and take back this design rapidly from concept to implementation is the motivation behind this project.Parallel Pa rking of a Car like Mobile Robot (CLMR)IntroductionThe following chapter discusses the kinematics model of a CLMR and investigates a feasible parking strategy using a fuzzy logic based controller.Kinematic Model of a CLMRThe kinematic model of a CLMR is as shown (Shih-Jie and Tzuu-Hseng 2002).The model assumes that the car wheels are in contact with the ground at all time i.e. no slip exists. is a mid point on the rear axle of the car is mid point on the line joining the front wheels is the angle between the X axis with respect to the fomite frame is the steering angle with respect to the vehicle frame is the distance between the front and rear axleThe non-holonomic constraint comparability is given by(2.1)The equation of the rear wheel is given by(2.2)(2.3)(2.4)The relationship between the rear and the front wheels are given byAnd (2.5)(2.6)The above equations are useful dapple finding the future positions of the CLMR.The physical layout of the CLMR is a three go around one. A t hree wheel robot and a four wheeled one have the same non- holonomic constraint. A three wheeled robot is chosen here for simplicity.The sensor positions are chosen with the arrogance that the parking position is always dismission to be to the right of the vehicle. 4 ultrasonic sensors are used. 2 sensors (s2 and s1) are placed on the right front and rear edge and the other 2 (s3 and s0) are placed at the front and rear sides of the car as shown in the Fig 2.2.The position of sensors s1 and s2 with respect toand described by the following equations.S1 (2.7)S2 (2.8)The position of the sensors is used to determine the distance of the CLMR from an obstacle during simulation.Parking StrategyThe parking strategy is developed intuitively as a human driver would do. The assumption is that a parking spot is somewhere to the right side of the vehicle to be parked (Holve and Protzel,1996). The vehicle would be moving parallel to the line of cars. The algorithm is as follows.Search for an obstacle free area on the right side of the car. The distance should be greater than the minimum distance required to park the car. This could be set at twice the length of the car.Once a sufficiently large parking spot is found, the car is reversed into the gap using a wall following algorithm. This algorithm aligns the car as parallel as possible in the gap without colliding with the rear wall.The car is then determined forward to align itself in the meat of the gap.Steps 2 and 3 can be repeated until the want position is reachedWall Following ascendency (Shih-Jie and Tzuu-Hseng , 2002)The heart of the parking strategy is a wall following controller.Shih-Jie and Tzuu-Hseng (2002) proposes a fuzzy logic scheme as follows.The variables shown in the figure represents the followingd1 distance between the rear end of the CLMR and the walld2 distance between the front end of the CLMR and the wallDist is the desired distance between the wall and the CLMRThe objective of the contro ller is get d1 equal to d2 which is the desired distance from the wall Dist .The scheme suggested is a 2 input one output scheme which controls the steering angle of the CLMR. Since the CLMR in this project is a three wheeled one the turning is done by varying the speeds of each wheel. The input variables to this control scheme are as follows if the CLMR is moving forward .Input1 = d2 Dist (2.9)Input 2 = d2 d1 (2.10)and if the CLMR is moving backwardInput1 = d1 Dist (2.11)Input 2 = d1 d2The output of the controller is the amount by which the CLMR needs to turn in a given direction.The fuzzy membership functions for Input1, Input2 and Steer are shown belowThe membership functions are equally divided triangular membership functions.The Rule Base for the wall following is shown in the table below. It is based on sliding mode control (Li et al, 2000)The rules can be an represented linguistically asIf (Input1 is cocksure Big) and (Input2 is Negative Big) then (steer is Zero)If (Inpu t1 is Positive Big) and (Input2 is Negative Small) then (steer is Positive Small) and so on25. If (Input1 is Negative Big) and (Input2 is Positive Big) then (steer is Zero)The defuzzication method used here is centre of gravity which is good for fine controlThe final fuzzy logic entire controller can be summarized as followsSummaryWith the kinematic equations of the CLMR and the equations describing the sensor positions, a model can be constructed. With the parking strategies and a fuzzy logic controller now developed we shall discuss an efficient methodology in pickings these designs into implementation.Model Based Design MethodologyOverviewThe advances in microprocessor technology in the early 70s brought about a change in the way control systems were developed. From electric relays built into ladder like networks and programmable logic controllers , controllers were being developed around a computer built with adequate hardware and software. Traditionally most of these control so ftware development was based on paper designs and manual computer programing followed by verification activities such as code inspections and a unit/ consolidation test (Guido Sandmann and Richard Thompson 2008).Many of these activities lack tool automation, and are very time consuming. Thus they are error prone and time consuming. Lack of tool chemical chain integration provides another opportunity for errors to be injected into the software that are often detected late and at high costs to the development process and so involve manual interaction. Model Based Design is software design methodology used to address these issues.The steps involved are in minimal brain damage ,some of the commercially available tools and the tool chain choices for this project are discussed belowModel Based Design ProcessThe Model Based Design process can be divided into the following steps (NI Developer Zone, 2006).System DefinitionSystem definition involves the design process of a particular probl em in consideration. It is mostly a conceptual design where in the problem and solutions are analyzed.Modelling SimulationThe step implies that the analyzed design is simulated using a graphical based simulation tool. MATLAB-SIMULINK, ASCET-MD etc are some examples of modelling tools. Designs here can be easily changed verified and re designed if necessary.Rapid PrototypingIf the hardware of production controller is not available during the design phase a generalized hardware controller maybe used to test some of the simulations. This is the rapid prototyping phase.TargetingTargeting refers to getting the software code executing on the production hardware.This stage involves the use of auto coders tools that generate C code directly of graphical models, sail compilers and other tool chains that facilitate this.Hardware in the loop TestingHardware in the Loop testing is used while developing control strategies for plant models like engines. It provides real time simulation of a re al world plant model.System TestingThis is the last step in the design process wherein the controller is tested on the real plant.SummaryOf the stages in Model Based Design discussed above, only the most relevant one will be applied to this project. Beginning with system definition, modelling simulation proceeding to targeting and finally onto system testing will be the stages followed in this project. MATLAB/SIMULINK, Stateflow, virtual(prenominal) Reality Toolbox, Fuzzy Logic Blockset would be the tools used for modelling and simulation while Real Time Workshop Embedded Coder would be used during targeting. A detailed list of other tools will be dealt in the next chapter.IntroductionLego Mindstorms NXT is a robotics kit which consists of mechanical building blocks and electronic sensors. The high spot of this kit is the programmable brick called the NXT. The NXT is a 32bit ARM7 based microcontroller which allows motors and electronic sensors to be easily interfaced .Users can al so run custom programs written via a GUI based programming language. The language provided by Lego, though very simple and intuitive to use does not exploit the true(a) potential of NXT. Over the years, advanced users of the Lego Mindstorms have been successful in creating custom tools that apply every imaginable concept of embedded systems.The following chapter discuss some of these software tools and hardware detail relevant to this project. The most important part of this is to be able to design , simulate and test the robot using MATLAB Simulink.supersonic SensorThe Lego Ultrasonic Sensor is shown below. It is mainly used for distance measurement. It has a range of 255 cm and is accurate to about +/- 3cmOne of the tasks of Model Based Design is to be able to program the NXT from Matlab Simulink environment. The tools required to automatise this process are discussed belowJohn Hansens Enhanced FirmwareFirmware in embedded systems term is referred to the basic software that is initially run on a hardware contrivance. It performs basics factions like initialization of devices , integrity checks on memory and so on similar to BIOS on a standard PC.The NXT brick , out of the box , has a standard firmware installed. This is designed to be used with the software provided. In order to write programs in C and realize the true potential of the NXT, a custom firmware has been written by John Hansen. This allows the NXT to be programmed by either using the provided software or by programming in C and using other tools which will be explained later.The NeXTToolThe NeXTTool is a program that allows communication between the host PC and the NXT brick. It can do mixed tasks, but most importantly it is used to download custom programs onto the NXT.The nxtOSEKnxtOSEK (previously known as LEJOS OSEK) is an open source real time operating system for LEGO MINDSTORMS NXT. It contains device drivers from leJOS NXJ, an open source program that allows users to write Java prog rams on the NXT and a real time operating system source know as a TOPPERS OSEK. C or C++ can be used to write custom programs and compiled with the nxtOSEK using the GCC tool chain (Takashi Chikamasa, 2008)The Embedded Coder Robot NXT Blockset is one of the key elements in applying Model Based Design Techniques to the Lego Mindstorms NXT. These blocks are custom Simulink blocks used in the controller side and serve as inputs and outputs to the real world. A word of caution. Custom blocks especially hardware related blocks behave different behaviour in simulation and code generation . For e.g. an ultrasonic sensor block will not directly give sensor readings. The behaviour of the ultrasonic sensor will have to be separately emulated.During code generation process, a function call to the ultrasonic device is placed where the block is used. This ensures the program using the block gets the value directly from the sensor. The details of the blocks used for this project are discussed be lowThere are two blocks that have to be for an ultrasonic sensor blocks consist of two blocks. The Ultrasonic Sensor Interface block and the Ultrasonic Sensor Read block.Servo Motor BlocksFig 4.5 Servo Motor BlocksThe working of the Servo Motor is similar to the ultrasonic block as explained above.The details of the block are given below.Data Typeint32Dimension1 1DataRange 0 to 255 cm, -1 (the sensor is not ready for measurement)Port IDS1/S2/S3/S4SummaryTools concerning with the implementation of a Matlab Simulink design on a Lego Mindstorms NXT were explored here. However the most important of all is to simulate a working design. The next chapter discusses the implementation of the parallel parking strategy and the simulation results.Parallel Parking SimulationAn integral part of Model Based Design is being able to simulate and get the desired results before implementation. The following chapter discusses the results of the controller developed in Chapter 2. For verification of th e controller a Simulink model developed by Takashi Chikamasa, (2006) was used. The model was of a 3 wheeled robot with the motor dynamics included. A 3-D environment was provided using the Virtual Reality Toolbox. The environment however has been changed to suit the parallel parking problem.Parallel Parking StrategyThe controller algorithm was developed in Simulink using Stateflow and the Fuzzy Logic Toolbox.A 2-D plot of the trajectory path traversed by the CLMR is shown below.Conclusion and RecommendationsThe parallel parking strategy implemented did show good results. However, real life scenarios may be very different. For example, the controller depends on wall following. This implies there has to be 3 sides closed in a gap for the parking controller to work.The development of the algorithm was with the intent of being able to execute it on a Lego Mindstorms Kit. However, there were some issues regarding this. The main being lack of a floating point unit on the NXT Brick. Though it is possible to execute floating point programs on the NXT, it causes severe performance issues.The controller developed used the fuzzy logic blockset which inherently uses a lot of floating point variables. One method to overcome this draw back could be developing a fixed point version of the fuzzy logic blockset.Other parking strategies involving classical control could be experimented with Lego Mindstorms. Complex tasks like path planning could be executed on a computer communicating with the NXT via Bluetooth and the control tasks could be implemented on the NXT using fixed point math.However, with Model Based Design, being able to write programs in Simulink, simulate it and be able to execute this on the Lego Mindstorms opens a whole new dimension of what can be done limited only by our creativityBibliographyC., Ho, C., Lin, S. Li, T. (2005). Omni-Directional Vision-Based Parallel-Parking Control Design for Car-Like Mobile Robot. minutes of the 2005 IEEE International Con ference on Mechatronics, 562-567.De Luca, A., Oriolo, G., and Samson, C. 1998. Feedback control of a nonholonomic car-like robot. In Robot Motion Planning and Control, ed. J.-P. Laumond, 171-253. Berlin Springer-VerlagDongbing, G. and H. Huosheng (2000). Wavelet neural network based predictive control for mobile robots. Systems, Man, and Cybernetics, 2000 IEEE International Conference on.Galijasevic, A. M. a. Z. 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