Bring Consciousness to Mobile Robot Being Localized

Mobile robot localization is one of the most important problems in robotics research. A number of successful localization solutions have been proposed, among them, the well-known and popular Monte Carlo Localization (MCL) method. However, in all these methods, the robot itself does not carry a notion whether it has or has not been localized, and the success or failure of localization is judged by normally a human operator of the robot. In this paper, we put forth a novel method to bring consciousness to a mobile robot so that the robot can judge by itself whether it has been localized or not without any intervention from human operator. In addition, the robot is capable to notice the change between global localization and position tracking, hence, adjusting itself based on the status of localization. A mobile robot with consciousness being localized is obviously more autonomous and intelligent than one without

Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL)

One of the challenging issues in robotics is to give a mobile robot the ability to recognize its initial pose ( position and orientation) without any human help. In this paper, the components of a mobile robot will be described in addition to the specification of the sensor that will be used. Then, the map of the environment  will be defined since it is pre-defined and stored in the memory of the robot. After that, a localization algorithm has been designed, analysed and implemented to develop the ability of a mobile robot to  recognize its initial pose. Finally, the final results that have been taken practically will discussed. These result will be divided into two main sub-sections; the first section describes the particles distribution over the working environment and their position update over a number of iterations. Second section will shows the update in the importance weight values over a number of iterations and for three different number of particles. 

Mobile robot localization failure recovery

Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased

Two improved methods for mobile robot localization

Mobile robot localization is the problem of determining the robot\u27s pose given the map of its environment, based on the sensor reading and its movement. It is a fundamental and very important problem in the research of mobile robotics. Grid localization and Monte Carlo localization (MCL) are two of the most widely used approaches for localization, especially the MCL. However each of these two popular methods has its own problems. How to reduce the computation cost and better the accuracy is our main concern. In order to improve the performance of localization, we propose two improved localization algorithms. The first algorithm is called moving grid cell based MCL, which takes advantages of both grid localization and MCL and overcomes their respective shortcomings. The second algorithm is dynamic MCL based on clustering, which uses a cluster analysis component to reduce the computation cost

An Improved Clustering based Monte Carlo Localization approach for Cooperative Multi-robot Localization

This thesis describes an approach for cooperative multi-robot localization based on probabilistic method (Monte Carlo Localization) used in assistant robots which are capable of sensing and communicating one with another. In our approach, each of the robots maintains its own clustering based MCL algorithm, and communicates with each other whenever it detects another robot. We develop a new information exchange mechanism, which makes use of the information extracted from the clustering component, to synchronize the beliefs of detected robots. By avoiding unnecessary information exchange whenever detection occurs through a belief comparison, our approach can solve the delayed integration problem to improve the effectiveness and efficiency of multi-robot localization. This approach has been tested in both real and simulated environments. Compared with single robot localization, the experimental results demonstrate that our approach can notably improve the performance, especially when the environments are highly symmetric