Mobile Robot Localization Based on Topological Model with Tiered Features

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.

Mobile robot navigation by image classification using a neural network

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.

Mobile robots have been the subject of research and experimentation for several decades. They combine mobility, sensor feedback, and smart control to achieve specific tasks-many times autonomously. One such task is autonomous charging. In this paper, the autonomous positioning of a differential-drive mobile robot above an inductive wireless charger is tested. Wireless charging is an increasingly popular technology for contactless power transfer, but it usually requires precise positioning of the receiver above the charger for effective charging to take place. The experimental mobile robot in this paper uses a camera sensor and computer-vison algorithm to approach a color target that marks the charger location. Then, the mobile robot uses misalignment-sensing coils to detect and correct small lateral misalignments with the charger. Measurements from the encoders on the robot wheels are used in a dual-loop controller featuring a linear quadratic regulator (LQR) to achieve the robot control objectives. The robot is successful in approaching and positioning itself within ±5 mm of the wireless charger center in several experimental trials. The hardware and control methods used in this experiment can be applied to similar systems to achieve autonomous wireless charging.

Currently, the application of control systems has been applied in various scientific fields including mechatronics and robotics. Applications in the branch of robotics are also growing day by day not only with conventional controls but also with intelligent systems. An autonomous robot in carrying out certain missions in an unknown environment requires information about the location itself and the environment through the map. A process to identify a position without a map is called a localization function on the robot. Mobile robots building maps and localization are two fundamental tasks when mobile robots work in indoor environments. With 2D laser scanning (LiDAR) data obtained in real-time, the robot can calculate the area of ​​all empty spaces in a room, then can choose the center of the room as its position for map building. The objective of this research is to implement a two-dimensional mapping method using LiDAR. The algorithm used in this study is the Gmapping Technique on ROS. The main purpose of this research is to map mobile robots with LIDAR sensors using the Robot Operating System for navigation and positioning of mobile robots. Through the actual experimental results, the mobile robot will move with a 2-dimensional mapping process.

In this paper, a multisensor fusion system that is used for calculating the position and orientation of an autonomous mobile robot is presented. The developed fusion system is distributed, robust, and asynchronous. It is distributed to permit the parallel function of all the sensors. It is robust because, being distributed, the system has been designed to keep working properly in spite of the failure, removal, or change of any sensor. It is asynchronous to take advantage of the different features and rates of each sensor. The implementation of the system is based on the distributed Kalman filter developed by Durrant-Whyte and Rao. In that distributed filter, all the sensors work in parallel to obtain their own estimate based on their own observations and on the observations coming from other sensors. Changes have been made to simplify and speed up the computation of the external validation equations and to allow the use of any sensor model. The equations have also been adapted to deal with asynchronously operating sensors and with the existence of communication delays. The fusion system is used to estimate the position and orientation of a mobile robot. The performance of the fusion system is shown both under simulation and in a real test with a mobile robot.

A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.

The navigation problem of controlling the accurate positioning of a mobile robot (MR) with a computer vision system under conditions of uncertainty of information about its position is considered. Visual servo control is used, in which the error signal is calculated as the difference in the coordinates of natural visual landmarks detected by the computer vision system on the current and reference (received in a target position) images. To compute the error signal, a probabilistic relaxation method of correct matching of the landmarks extracted in the image is proposed. The efficiency of the proposed method has been confirmed by numerical experiments for processing real images.

Aiming at the positioning and navigation system of indoor mobile robot, a fuzzy control method of indoor location navigation based on UWB communication technology is proposed. Firstly, based on the UWB radio signal, the mobile robot localization information is obtained by the twice triangular centroid localization algorithm, and the reactive fuzzy controller is used to realize the local path planning. An improved contour following method is proposed to make the mobile robot get out of the trap area quickly and reach the target. This method can effectively drive the mobile robot to reach the target in a complex and unknown environment with reasonable path. Finally, a fourwheel indoor mobile robot is taken as an example in the indoor environment of 10m × 10m. The experiment proves that the method can effectively drive the mobile robot to reach the target point in the unknown environment with the better path.

A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

We have developed a dead reckoning sensor system and a tracking algorithm for mobile robots to estimate the path when a mobile robot explores an unknown, enclosed region where GPS access or landmarks are unavailable. A dead reckoning sensor system consists of a low-cost MEMS IMU and a navigation sensor (used in laser mice), which provide complementary functions. The IMU has benefits such as compact size, a self-contained system, and an extremely low failure rate but has a bias drift problem, which can accumulate substantial error over time. A navigation sensor measures the motion of a mobile robot directly without the slip error in the case of a wheel-type odometer, but it often fails to read a surface. A tracking algorithm consists of an extended Kalman filter (EKF) to fuse data from the IMU and the navigation sensor and a least-squares method to estimate acceleration bias in the EKF. We obtained experimental data by driving a radio-controlled car equipped with the sensor system in a 3D pipeline and compared the path estimated by the tracking algorithm with the path of the pipeline. The tracking algorithm combined data from the IMU and the navigation sensor and correctly estimated the path of the radio-controlled car. Our study can be applied to estimate position or path of mobile robots without external aids such as GPS, landmarks, and beacons.

This paper presents the image processing algorithm for wireless charging between each mobile robot. The image processing algorithm converts Red Green Blue (RGB) format of inputted image to detect edge. It calculates a specific area using Hough Transformation (HT) in detected edge and judges correct charging antenna using Speeded-Up Robust Features (SURF). Accordingly, the image processing algorithm can control position and direction of mobile robot and antenna for wireless charging. The image processing algorithm is implemented wireless charging systems, which are set up on each two mobile robot and it is verified with experiment.

Localization is an important topic in mobile robots. It is essential that a mobile robot plans movement and reaches goals. In this paper, we described self-localization technique for mobile robot based on particle filtering in active beacon system. The basic method is to estimate value of position and heading of mobile robot using ultrasonic sensor as particle filter is used to eliminate process and measurement noise. Several variants of the particle filter such as SIR and RPF are introduced. These are discussed and compared with EKF for localization of mobile robot.

For better auto-positioning and navigation of robot, this paper proposes a mobile robot navigation method based on radio frequency identification (RFID) technology. The RFID readers were mounted on the robot. Then, the author developed a double positioning algorithm for the position and orientation adjustment, with the aim to guarantee the smooth movement of the robot from the start point to the terminal and to reduce the error in robot motion. The experimental results show that the proposed algorithm has successfully realized the auto navigation and positioning of mobile robot.