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In this project, we will introduce a navigating robot, which will navigate its way around the RFID based floor without the help of a human. The objective of this project is to use data collected from RFID tags, which are embedded on the ground and use it to navigate a robot. The robot would be able to find specific locations and execute specific tasks based on the information that it collects from the tags. Robot is also being used in many similar cases to increase the speed, accuracy, and efficiency, while decreasing the losses as a result of job injury. The vehicle is equipped with IR Transmitter and IR Receiver which is used to find the obstacle in the path of the Vehicle, destination position information is obtained by using GPS receiver, for dynamic destination changes a GSM modem is equipped. Controlling commands for Motor Driver like turn left, turn right, speed up and speed down etc., interfaced by using low cost LPC2148 microcontroller into RFID. Then by using moving control commands which are read from already stacked RFID tags on the tracks the autonomous mobile robot will successfully reach the destination .
Keywords- Radio Frequency Identification, Industrial Automation, RFID Reader, RFID Tag, ARM LPC 2148 Microcontroller, Autonomous Mobile Robot, Novel Navigation Technique. GPS, GSM .
Autonomous robots with mobile capability are finding their place in numerous application fields. Some typical examples of these application fields are factory automation, service application, and hazardous environments such as dangerous zones in nuclear power stations, space exploration, material handling in hospital and security guarding.
Navigation is the key requirement for performing these tasks . Navigation is the ability of a mobile robot to reach the target safely without human assistance. Thus the main issues that need to be addressed in mobile robot navigation are reactive obstacle avoidance and target acquisition.
To provide detailed information of environment which may not be available using combinations of other types of sensors and has been addressed by many researchers a popular method is used i.e vision based sensing for autonomous navigation .
In this the navigation task is subdivided into hurdle avoidance and goal seeking tasks. With the help of two front IR sensors Hurdle avoidance is achieved.
The range data collected from these sensors are fed to inside the microcontroller. Goal seeking behavior involves the data from GPS, GPS receiver which is processed by another microcontroller. In this project for the demo purpose we are giving the goal as left, right, forward commands because in room environment there will be small changes in the GPS location coordinates that robot may not consider it as different location .
The main microcontroller fetches the desired data and generates motion commands for robot. For selecting start and goal stations for robot inside the campus a GSM modem is interfaced to the main controller.
In the past the robot is navigate based on the neural network in unknown environments by using GPS and GSM which is used for goal position information and where the exact position of the robot . the information is fed into the 8051 microcontroller by using RS 232 serial interface and with the help of sensors it can detect the obstacle and move side and continue the navigation they use neural network for robot motions . the neural network used in that is the multilayer feed forward neural network with back propogation training algorithm.
MULTILAYER FEED FORWARD NEURAL NETWORK
These networks are trained with back propogation training algorithm. the network consists of neurons the first layer is the input layer and the output layer is the last layer in between the two layers hidden layers are there.
BACK PROPOGATION ALGORITHM
To train artificial neural networks back propagation is the common method. it is a supervised learning method and a generalization of delta rule. for making up the training sets for it requires many inputs for a desired set of output.
the propagation can be divided into two phases there are propagation and weight update.
there are two types of propagation i.e forward and backward. in forward propagation to generate the propagation's output activations the training pattern's input are fed through the neural network.
In Backward propagation the deltas of all output and hidden neurons the propagation's output activations through the neural network using the training pattern.
A four wheeled car type vehicle robot is selected for experimentation which is a modified version of readily available RC car.
To change the destination place on run time an SIM300D GSM modem is used. To communicate with the GSM modem AT commands are used by the microcontroller.
The modem also informs the central station about the track history of the vehicle and any emergency situation occurred.
IR SENSOR MODULE
Fig 1: system architecture
3. GPS Receiver
To get position information of robot in the form of latitude and longitude values an M89 GPS receiver is used.
The GPS (global positioning system) provides position information depending on the longitude and latitude. The longitude and latitude position is accompanied with a letter indicating where the position is located relative to the original longitude line of GMT and the latitude circle of equator. Letter 'N' means to the north of the equator, 'S' south of the equator, 'W' west of GMT and 'E' east of GMT.
Objectives of the Function
This function is supposed to give the angle between the direction of the vehicle and the direction of the target GPS point by using trigonometric functions. This function will use the value acquired by the compass in order to calculate the current orientation and the target orientation.
The specifying of which quarter of the globe the vehicle is located is essential since we need it to determine the quadrant of the target
GPS point relative to the current GPS. This is explained as follows:
Suppose that the vehicle is located at a random position represented by and the next GPS. We need first to determine the quadrant at which the next GPS point is located.
An autonomous vehicle must be equipped with sensing and controlling equipment that provide ability to avoid the obstacles and perceive the course environment. No base stations allowed for positioning accuracy is allowed.
The RFID grid in which the robot will be navigating in is an area that measures 120cm by 120cm. The RFID tags are place in a 5x5 configuration. The spacing between tags is 30cm, roughly the distance of 3 antenna lengths. Horizontal rows and vertical columns are numbered 1 through 5.
Figure (2) shows the layout of the RFID grid. This was deemed to be the optimal configuration based on the range of the RFID reader. During initial testing, the reader demonstrated a reading range of approximately 3cms. This limited the distance we could space the tags from each other. If the tags were spaced any further from each other, the probability of the robot not hitting a tag would increase.+
Fig 2 RFID Grid
Navigating in the grid is a systematic process. It is assumed that the robot is first placed on the outer edges of the RFID grid, facing the center. The robot will then proceed to navigate itself and find the tag located in row 1, column 3.The navigation algorithm starts by first determining what direction the robot is facing, and then selects the appropriate sub-algorithm.
The flowchart to determining direction is shown in Figure (3). When the robot is first placed anywhere on the edge of the grid, facing the center, the RFID microcontroller obtains tag information from reader. This tag information is the identification number found on the tag. The RFID microcontroller then matches the retrieved tag information to a pre-assigned list of where this particular tag will be found on the grid.
The tag value from the pre-assigned list is the information that will be sent to the NAV microcontroller (the microcontroller tasked to perform the navigation algorithm). The converted tag information will now be labeled as the previous tag info and put into the navigation algorithm memory for use later in the process.
Fig (3) Direction Finding Flowchart
At different radio frequencies, several versions of RFIDs are operated. Basically Low frequency(125KHz-134KHz), Midfrequency(13.56MHz),andUltra high frequency(850MHz-950MHz) bands are allocated for RFID.
For example, while the robot moves to tag 1 and receives the commands of turn left and speed up, then the MCU will make some control actions to let the robot conform the commands . the robot moves to tag 2, the commands of go straight and slow down were received, the MCU will once again make some control actions to let the robot conform the commands. Therefore, the robot will then move in moving path 1 automatically.
Of course, the robot can also move in the other paths according to the commands received from tags. Fig.4 shows the physical hardware of the proposed RFID-based autonomous mobile robots.
Fig(4) Driving Circuit of the autonomous Mobile robot
Infrared Sensors or IR Sensors
Electromagnetic waves which have wavelengths longer than visible light wavelengths and smaller than microwaves is Infrared radiation ,which are invisible to human eyes.
In Electromagnetic spectrum 75Âµm to 1000 Âµm is the infrared region. Near infrared region occupies 0.75Âµm to 3Âµm, mid infrared region is from 3 Âµm to 6 Âµm and above 6 Âµm is far infrared region of electromagnetic spectrum..
A motor driver IC L293D (usable from 6-12V, total output current-1A) is more convenient to use in this project.
The Microcontroller we are chosen here is LPC2148 ARM controller. This ARM controller consists of two RS232 ports which is able to communicate with GPS and GSM modem at a time. The operating voltage of ARM is 3.3v which will be done in power circuit block. The target location values (Longitude & Latitude) are sent as message to GSM module which is connected to the Controller. The controller will analyze the current position values of the vehicle and comp area it to destination position value and make the motors to rotate such that it reaches the Destination. When going to the destination if there is any obstacle it will be observed by using IR Transmitter and Receiver and make the vehicle to rotate and move to destination position. Once the vehicle reaches the destination the Buzzer will switches on.
In this paper, design of autonomous vehicle is presented for transportation of light weight equipment inside the university campus. Equipped with various sensors, the vehicle has the capability of navigating in complex environments avoiding the obstacles in its way and reaching the target. The complexity of the system is reduced by making it modular i.e., more modules can easily be added to system by setting their priority level in the main controller. This low cost solution can also be used in wheel chairs as a navigation aid for disabled persons.