RFID-based indoor positioning of autonomous aid for disable people

Nowadays, global positioning system (GPS) is widely used in localization area because it’s very capable and reliable. However, in indoor positioning, GPS capabilities are very limited since the satellite signals are typically strongly attenuated by walls and ceiling. Thus, this project introduced the concept which presents a self-localization of a mobile robot by fusing radio frequency identification (RFID) system and wireless communication using XBee module to be used in indoor environment. Two Xbee devices will be used to transfer data from the remote control unit to mobile robot. Aims of this project are to create a mobile robot that reacts to the remote control to go to the desired position as command. To meet the desired aim of this project, practical and compact design technique are emphasized in order to create a mobile robot and the remote control. Sixteen RFID cards are arranged in a fixed pattern on the floor. A unique code of each RFID card provides the position data to the mobile robot. An RFID reader act as antenna will be installed to read the card data on the below of the mobile robot. The user can make it come by easily pressing the remote control by informing the user location. Keywords: RFID, RFID reader, RFID tag, indoor location identification, mobile robot, remote control, navigation, wireless communication

RFID-based indoor positioning of autonomous aid for disable people

Nowadays, global positioning system (GPS) is widely used in localization area because it's very capable and reliable. However, in indoor positioning, GPS capabilities are very limited since the satellite signals are typically strongly attenuated by walls and ceiling. Thus, this project introduced the concept which presents a self-localization of a mobile robot by fusing radio frequency identification (RFID) system and wireless communication using XBee module to be used in indoor environment. Two Xbee devices will be used to transfer data from the remote control unit to mobile robot. Aims of this project are to create a mobile robot that reacts to the remote control to go to the desired position as command. To meet the desired aim of this project, practical and compact design technique are emphasized in order to create a mobile robot and the remote control. Sixteen RFID cards are arranged in a fixed pattern on the floor. A unique code of each RFID card provides the position data to the mobile robot. An RFID reader act as antenna will be installed to read the card data on the below of the mobile robot. The user can make it come by easily pressing the remote control by informing the user location

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Cooperative Localization under Limited Connectivity

We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic~experiment.Comment: 9 pages, 5 figure

A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments

Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009

Monocular navigation for long-term autonomy

We present a reliable and robust monocular navigation system for an autonomous vehicle. The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS. Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach. In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled. We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound. The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes. This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation

Simultaneous maximum-likelihood calibration of odometry and sensor parameters

For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for external sensors or devices. Moreover, it is not necessary to drive the robot along particular trajectories. The available data are the measures of the angular velocities of the wheels and the range sensor readings. The maximum-likelihood calibration solution is found in a closed form