Navigation in mobile robots based on Markov localization

In our age, the use of mobile robots is spread over a very wide area. Mobile robots avoiding obstacles in achieving the goal plan are fulfilled tasks such as tracking a particular way that spaces are used. Loaded mobile robots in order to perform these tasks themselves, must know where they are. Therefore, a mobile robot with localization calculations "Where am I?" sought to answer the question. For these reasons, positioning data has an important place for using mobile robots effectively. A robot does not possess natural senses like human beings have. Human beings get information about their surroundings through vision and other sensing powers. A robot cannot explore an unknown environment unless it is provided with some sensing sources to get information about the environment. Different kinds of sensors such as sonars, odometers, laser range finders, inertial measurement units (IMU), global positioning system (GPS) and cameras are used to make a robot capable of sensing a wide range of environments. The map of the environment is a basic need of a robot to perform indoor services like moving room to room, gripping and picking an object from one place and taking it to another place. To perform such type of services, the robot should not only know about the environment but while it is moving it should also be aware of its own location in that environment. Mobile robot localization can be seen as a problem of coordinate transformation. Maps are described in a global coordinate system, which is independent of robot's position. Localization is the process of establishing correspondence between the map coordinate system and the robot's local coordinate system. Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics.The aim of localization is to estimate the position of a robot in its environment, given a map of the environment and sensor data. Most successful mobile robot systems to date utilize localization, as knowledge of the robot's position is essential for a broad range of mobile robot tasks. A lot of method have been proposed for mobile robots where are located to obtain information. These methods may vary in terms of both applied methods that are used for both data and maps. These are known as relative localization, and absolute localization. Relative localization, evaluating the position and orientation using information provided by various on board sensors (e.g. encoders, gyroscopes, accelerometers etc). Absolute localization, obtaining the absolute position using beacons, landmarks or satellite based signals. To increase the accuracy of the positioning process, these two methods are applied together. One of the most known is odometry which a method of determining the position. The classical technique for a wheeled robot to calculate its position is to track its location through a series of measurements of the rotations of the robots' wheels, a method often termed odometry. Odometry requires a method for accurately counting the rotation of the robot wheels. A standard method for doing this is to instrument the wheels with optical shaft encoders. But odometry method, the processing of the data from the wheels made with this process, errors in positioning may be present. The reason for this is to skid on the ground or the wheels is slipping. For these reasons, the positioning operation cannot be carried out correctly. Probabilistic approaches in mobile robot positioning process over time came into effect. Probabilistic localization the robot by processing the information it receives from the environment by increasing or decreasing the likelihood of the location being where to identify. There are various cases in probabilistic positioning. This thesis has been investigated and described several situations. Markov localization method which is preferred in this thesis with a static environment where the initial of robot's position is known. Markov localization addresses the problem of state estimation from sensor data. Instead of maintaining a single hypothesis as to where in the world a robot might be, Markov localization maintains a probability distribution over the space of all such hypothesis. Markov localization method, there is a belief that the robot has. This is actually the probability distribution function of the robot at that location. The mobile robot starts with an initial belief. In this thesis, the robot has an initial belief. Determination of the robot position we use odometric data from wheels and external sensor data together.That is the requirement of Markov localization. The Markov localization is the implementation of two separate modelling process. The first one is act modelling, the represents the estimation of actual position of the mobile robot based on the actual information from odometry method. The second one is observe modelling, the represents the improved estimation of the position based on the actual measurements of other sensors. The vehicle which is used in this thesis has a differential drive system. A differential wheeled robot is a mobile robot whose movement is based on two separately driven wheels placed on either side of the robot body. It can thus change its direction by varying the relative rate of rotation of its wheels and hence does not require an additional steering motion. Differential drive system studied and probabilistic model were applied. In the implementation of thesis we use laser range finder sensor which is external sensor. A laser range finder which uses a laser beam to determine the distance to an object. The most common form of laser range finder operates on the time of flight principle by sending a laser pulse in a narrow beam towards the object and measuring the time taken by the pulse to be reflected off the target and returned to the sender. LMS 200 laser range finder used in this implementation. Detailed information about the LMS 200 laser sensor are given in the thesis. In this thesis our objective is to perform probabilistic Markov based navigation to resolve the error which is coming from processing odometric data. For all possible localization Markov method calculates probability distribution function for belief. Therefore mobile robot has a belief about where it is. At the end we compare different situations for robot. We create a static environment and get it's map. We apply three different method to localizing the mobile robot. We choose a scenario. Mobile robot avoid from obstacles and arrive the target point. For getting and comparing result give rightful situations. In application we gave the same target point to the mobile robot and the results are compared in terms of error rates.