Mobile robot localization using two sonar sensors and one natural landmark

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

A low cost system for the localization of mobile indoor robots is presented. The system is composed of an emitter located on a wall and a receptor on top of the robot. The emitter is a laser pointer acting like a beacon, and the receptor is a cylinder made out of 32 independent photovoltaic cells. The robot's position and orientation are obtained from the moments when the laser crosses each cell.

We propose a novel algorithm for improved localization of the mobile robots by fusing RFID localization system and ultra-sonic sensors. Because there are uncertainties in previous RFID localization system, we are focusing on the sensor fusion system, which removes the uncertainty of the robot location using the distance measurements by ultra-sonic sensors. We divide into 2 parts, which are GPE (global position estimation) by RFID localization system and LEC (local environment cognition) by ultra-sonic sensor. The scheme to remove the uncertainty is proposed, which are the modeling about the measurement noise for RFID localization system and to estimate the straight obstacle and circular obstacle in well-structural indoor environment by the ultra-sonic sensors. The hierarchical localization algorithm is proposed to estimate the position of the mobile robot from GPE and LEC and for robust localization under the unknown indoor environment.

This paper describes a method of producing high resolution maps of an indoor environment with an autonomous mobile robot equipped with sonar range finding sensors. This method is based upon investigating obstacles in the near vicinity of a mobile robot. The mobile robot examines the straight line segments extracted from the sonar range data describing obstacles near the robot. The mobile robot then moves parallel to the straight line sonar segments, in close proximity to the obstacles, continually applying the sonar barrier test. The sonar barrier test exploits the physical constraints of sonar data, and eliminates noisy data. This test determines whether or not a sonar line segment is a true obstacle edge or a false reflection. Low resolution sonar sensors can be used with the described method. The performance of the algorithm is demonstrated using a Denning Corp. Mobile Robot, equipped with a ring of Polaroid Corp. Ultrasonic Rangefinders.

Mobile robot navigation by image classification using a neural network

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. Previously we developed a robot localisation system, namely the Gaussian Mixture of Bayes with Regularised Expectation Maximisation (GMB-REM), which introduced sensor selection task. GMB-REM allows a robot's position to be modelled as a probability distribution, and uses Bayes' theorem to reduce the uncertainty of its location. In this paper, a new sensor selection task incorporated with sensor fusion is proposed, namely an evolved form of GMB-REM. Empirical results show the new sensor selection method outperforms GMB-REM with the previous sensor selection. Especially, in this paper, we illustrate that the new system is able to significantly constrain the error of a robot's position.

Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly affected by the inaccuracy of the sensors. These inaccuracies are caused by various factors which includes internal interferences of the sensor and external environment noises. In order to overcome these noises, a filtering method is required in order to improve the mobile robot’s localization. In this research, a 2- wheeled-drive (2WD) mobile robot will be used as platform. The odometers, inertial measurement unit (IMU), and ultrasonic sensors are used for data collection. Data collected is processed using Kalman filter to predict and correct the error from these sensors reading. The differential drive model and measurement model which estimates the environmental noises and predict a correction are used in this research. Based on the simulation and experimental results, the x, y and heading was corrected by converging the error to10 mm, 10 mm and 0.06 rad respectively.

Robot navigation and obstacle avoidance are from the most important problems in mobile robots, especially in unknown environments. The autonomous navigation of mobile robots has attracted a number of researchers over the years. A wide variety of approaches and algorithms have been proposed to tackle this complex problem. There are many successful localization methods that can determine the robot's position relative to a map using sonar, laser and camera data; however, most localization methods fail under common environmental conditions. In this paper we present a novel method for navigation of autonomous mobile robots in several indoor environments that optimize our old navigation approach, using fuzzy Kalman filter based on extracted lines and fusing data from laser scanner sensors. In this approach, the sonar data and laser measurement are combined using the fuzzy Kalman filter. Finally, the simulation results show that the proposed algorithm offers advantages over previous methods and has improved performance.

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.

This paper addresses a radio-frequency identification (RFID)-based mobile robot localization which adopts RFID tags distributed in a space. Existing stand-alone RFID systems for mobile robot localization are hampered by many uncertainties. Therefore, we propose a novel algorithm that improves the localization by fusing an RFID system with an ultrasonic sensor system. The proposed system partially removes the uncertainties of RFID systems by using distance data obtained from ultrasonic sensors. We define a global position estimation (GPE) process using an RFID system and a local environment cognition (LEC) process using ultrasonic sensors. Then, a hierarchical localization algorithm is proposed to estimate the position of the mobile robot using both GPE and LEC. Finally, the utility of the proposed algorithm is demonstrated through experiments.

The objective of this research is to develop a real time obstacle detection and obstacle avoidance for autonomous navigation of mobile robots using a stereo camera in an unstructured environment. Autonomous navigation of mobile robots demands a) Exact determination of position and orientation of Robot and b) Accurate determination of size, shape, depth and range of potential obstacles in the environment. Simple kinematic model is used for mobile robot and stereo camera with pan and tilt provision is considered for long range operation. Complete 3D reconstruction of object/obstacle is obtained from the stereo matching algorithm and with triangulation method. The pose (position and orientation) of mobile robot is formulated from the static object observation with stereo reference matching points using RANSAC (RANdom SAmple Consensus) in successive frames. Potential field based obstacle avoidance formulation is carried out by using the obstacle range, size information, mobile robot position and orientation. Finally, proportional derivative navigation control loop along with obstacle avoidance algorithm is formulated and verified.

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

Mobile robot localization via EKF and UKF: A comparison based on real data

Localization of an autonomous mobile robot has always been a topic of interest in Robotics community. The constraints of working under different environments are constantly a challenge in designing an efficient localization system. We propose a localization algorithm for a mobile robot, which will be used for home security. The objective is to perform indoor localization efficiently and keeping the cost as low as possible so as to make it commercializable. Here, we introduce an approach based on recognizing a pattern generated by laser pointers on the wall, which is captured by an infrared camera. The image of this pattern is used to locate the position of a robot in the room. The problem of working under black ceiling has been overcomed because the projection is on the wall and is moving which also increases the range of operation. As the pattern is unique, the interference of strong sunlight has also been fixed to some extent. Experimental results show that the proposed approach is effective in real environment and opens new doors for future research.