Hung-Hsing Lin

Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering

This paper presents methodologies and technologies for ultrasonic localization and pose tracking of an autonomous mobile robot (AMR) by using a fuzzy adaptive extended information filtering (FAEIF) scheme. A novel ultrasonic localization system, which consists of two ultrasonic transmitters and three receivers, is proposed to estimate both the static and the dynamic position and orientation of the AMR. FAEIF is presented to improve estimation accuracy and robustness for the proposed localization system, while the system lacks sufficient information of complete models or the process and measurement noise varies with time. Static pose estimation that utilizes the averaging approach is also investigated. Six time-of-flight ultrasonic measurements, together with the vehicles dead-reckoned localization information, are merged to update the vehicles pose by utilizing the FAEIF sensor fusion algorithm. The proposed algorithms were implemented using an industrial personal computer with a computation speed of 800 MHz and standard C++ programming techniques. The system prototype, together with computer simulations and experimental results, was used to confirm that the system not only provides precise estimation of both the static and dynamic pose of the AMR but also provides a simple economical structure for navigational use and installation/calibration..

Ultrasonic self-localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering

This paper develops methodologies and technologies for ultrasonic self-localization of an autonomous mobile robot (AMR) using a fuzzy adaptive extended information filtering scheme. A novel ultrasonic localization system consisting of two ultrasonic transmitters and three receivers, is presented to estimate both the static and dynamic position and orientation of the AMR. A fuzzy adaptive extended information filter (FAEIF) is presented to improve estimation accuracy and robustness for the proposed localization system, while the system lacks of sufficient information of complete models of the process and measurement noise varies with time. A static pose estimation utilizing the averaging approach is investigated as well. Six time-of-flight ultrasonic measurements together with the vehicles dead-reckoned location information are merged to update the vehicles dead-reckoned location information are merged to update the vehicles pose by utilizing FAEIF sensor fusion algorithm. The proposed algorithm were implemented using an industrial personal computer with a computation speed of 800 MHz, and standard C++ programming techniques. The system prototype together with computer simulations and experimental results has been used to confirm that the system not only provides precise estimation of both the static and dynamic pose of the AMR, but also provides a simpler and more economical structure for navigation use and installation/calibration.

Trajectory tracking control of a laser-guided wheeled mobile robot

This paper develops methodology and technique for design and implementation of a path tracking control of a wheeled mobile robot using a retroflective laser scanner. A fuzzy extended information filtering scheme is presented to process the measurements to obtain the best estimates of the current position and orientation of the robot. A globally nonlinear and asymptotically stable kinematic path tracking control is developed to steer the vehicle to follow the desired trajectories without errors. Computer simulations and experimental results are performed to illustrate the feasibility and effectiveness of the proposed self-localization and control methods.

Multisensor 3D posture determination of a mobile robot using inertial and ultrasonic sensors

Abstract This paper presents methodologies and techniques for fusing inertial and ultrasonic sensors to estimate the current posture of a mobile robot navigating over indoor uneven terrain. This new type of pose tracking system is developed by means of fusing an inertial navigation subsystem (INS) and an ultrasonic localization subsystem. Extended Kalman filtering (EKF)-based algorithm for integrating both the subsystems is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage and surface roughness. Experimental results are conducted to illustrate feasibility and effectiveness of the proposed system and method.

Improved global localization of an indoor mobile robot via fuzzy extended information filtering

Global localization of mobile robots has been well studied using the extended Kalman filter (EKF) method. This paper presents a fuzzy extended information filtering (FEIF) approach to improving global localization of an indoor autonomous mobile robot with ultrasonic and laser scanning measurements. A real-time FEIF algorithm is proposed to improve accuracy of static global pose estimation via multiple ultrasonic data. By fusing odometric, ultrasonic, and laser scanning data, a real-time FEIF-based pose tracking algorithm is developed to improve accuracy of the robots continuous poses. Several experimental results are performed to confirm the efficacy of the proposed methods.

Global Posture Estimation of a Tour-guide Robot using RFID and Laser Scanning Measurements

This paper presents methodologies concerning with how to apply a least-square method and an extended information filtering (EIF) scheme to global posture estimation of an tour- guide robot with radio-frequency-identification-device (RFID) and laser scanning measurements. An active RFID localization system with the RSSI (received signal strength indication) data from selected tags to a reader is presented to estimate both the unknown start-up position and orientation of the tour-guide robot at any circumstance. With the odometric data from the driving wheels and the laser scanning measurements from the robots surroundings, an EIF-based pose localization algorithm is proposed to continuously keep track of the robots poses at slow speeds. Experimental results are conducted to confirm that the proposed method not only provides precise estimation of both the unknown initial and continuous moving poses of the tour-guide robot, but also shows a simpler and more efficient localization way for navigation purposes.

Posture estimation and tracking of an autonomous mobile robot using a laser scanner with retro‐reflectors

Abstract This paper develops methodologies and techniques for posture estimation and tracking of an autonomous mobile robot (AMR) using a laser scanner with at least three retro‐reflectors. A novel three‐point triangulation method using the laser scanner is presented to find an initial posture of the robot and then an extended information filtering (EIF) method is used to improve the accuracy of the robots posture estimation. The sensitivity to measurement errors with respect to different reflector arrangements is investigated as well. With the odometric information from the driving wheels, an EIF‐based posture tracking algorithm is proposed to continuously keep track of the robots posture at slow speeds. Simulation and experimental results are compared to show the efficacy and usefulness of the proposed method. The proposed method can be applied to several mobile robots navigating over flat terrain in the areas of manufacturing factories, offices, hospitals, homes and etc.

Global pose localization of an autonomous wheeled mobile robot by fusing active RFID and ranging laser scanner: A fuzzy extended information filtering approach

The paper presents a global pose localization method for an autonomous mobile robot (AMR) in indoor environments by fusing measurements from an active RFID and a ranging laser scanner. The relations between the received signal strength indication data from a selected tag to a reader and the distances from the tag to the reader are established utilizing the curve-fitting method and the fuzzy weighted least-squares method at some typical working environments of the robot. A static global pose initialization algorithm is presented to estimate both the unknown start-up position and orientation of the autonomous mobile robot utilizing a fuzzy extended information filtering approach. A fuzzy extended information filtering pose tracking algorithm is then proposed to estimate the moving poses of the AMR. Simulations and experimental results are used to show the performance and merit of the proposed method.

3D Posture determination of a wheeled mobile robot by fusing dead‐reckoning and ultrasonic measurements

Abstract This paper develops a methodology and technique for three‐dimensional (3D) posture determination of a mobile robot over uneven terrain. The proposed self localization system is developed by means of integrating a 3D dead‐reckoning (DR) subsystem together with a novel ultrasonic localization subsystem for indoor navigation. The extended‐Kalman‐filter (EKF)‐based mutilsensory fusion method is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage, surface roughness and wheel misalignment. Experimental results are performed to illustrate the feasibility and effectiveness of the proposed system and method.

Automatic mapping for an indoor mobile robot assistant using RFID and laser scanner

This paper presents an automatic mapping method of an indoor mobile robot assistant for the elderly people. Mapping based on the minimalistic environmental model is accomplished automatically by using a laser scanner which works at 500 K baud rate, and a RFID which is adopted to initialize robots initial posture in real time. Numerous simulations and experimental results are performed to illustrate the feasibility and effectiveness of the proposed methods.