Odometry Correction of a Mobile Robot Using a Range-Finding Laser

Two methods for improving odometry using a pan-tilt range-finding laser is considered. The first method is a one-dimensional model that uses the laser with a sliding platform. The laser is used to determine how far the platform has moved along a rail. The second method is a two-dimensional model that mounts the laser to a mobile robot. In this model, the laser is used to improve the odometry of the robot. Our results show that the one-dimensional model proves our basic geometry is correct, while the two-dimensional model improves the odometry, but does not completely correct it

Mobile robot positioning: Sensors and techniques

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search of a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This article provides a review of relevant mobile robot positioning technologies. The article defines seven categories for positioning systems: (1) Odometry, (2) Inertial Navigation, (3) Magnetic Compasses, (4) Active Beacons, (5) Global Positioning Systems, (6) Landmark Navigation, and (7) Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this article serve only to represent their respective categories, but they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevily, not all could be cited in this article. © 1997 John Wiley & Sons, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34938/1/2_ftp.pd

The Odometry Error of a Mobile Robot with a Synchronous Drive System

This paper presents an error modeling of an odometry system for a synchronous-drive system and a possible strategy for evaluating this error. The odometry error is modeled by introducing four parameters characterizing its systematic and nonsystematic components (translational and rotational). The nonsystematic errors are expressed in terms of a covariance matrix, which depends on both the previous four parameters and the path followed by the mobile robot. In contrast to previous approaches which require assuming a particular path (straight or circular) in order to compute this covariance matrix, here general formulas are derived. We suggest a possible strategy for simultaneously estimating the four model parameters. As we will show, our strategy only requires measuring the change in the orientation and position between the initial and final configurations of the robot, related to suitable robot motions. In other words, it is unnecessary to know the actual path followed by the robot. We illustrate the proposed strategy by discussing the accuracy of the parameters estimation and by showing some experimental results obtained with the mobile robot Nomad150

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential driv

Estimating the Odometry Error of a Mobile Robot during Navigation

This paper addresses the problem of the odometry error estimation during the robot navigation. The robot is equipped with an external sensor (like laser range finder). Concerning the systematic error an augmented Kalman Filter is introduced. This filter estimates a vector state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from the external sensor as observations. The estimation of the non-systematic component is carried out through another Kalman Filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman Filter. Both synchronous and differential drive systems are considered

Calibration of Mobile Robot with Single Wheel Powered Caster

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 지능정보융합학과, 2022. 8. 박재흥.모바일 로봇의 제어와 오도메트리에 큰 영향을 주는 기구학적 파라미터를 보정하는 기구학적 캘리브레이션 방법은 다양한 종류의 모바일 로봇에서 연구되어왔다. 기구학적 캘리브레이션 방법은 모바일 로봇의 종류에 의존적이기 때문에 각 종류에 맞는 기구학적 캘리브레이션 방법이 필요하다. 캐스터 기반 모바일 로봇의 경우 복잡한 기구학적 형상 때문에 기구학적 파라미터가 부정확한 경우 제어 시 응력을 발생시켜 미끄러짐을 유발하기 때문에 정확한 기구학적 파라미터를 아는 것이 중요하다. 캐스터 기반 모바일 로봇을 위한 기구학적 캘리브레이션 방법은 특정 모델인 분할 캐스터에 한하여 연구가 진행되었다. 이전 연구는 캐스터 바퀴를 고정한 경우 바퀴와 바닥 사이에 회전이 일어나면 안 되기 때문에 바닥과 1점 접촉을 하는 단일 바퀴 캐스터에는 적용할 수 없다. 본 논문은 단일 바퀴 캐스터 기반 모바일 로봇의 정확한 기구학적 파라미터를 구하는 기구학적 캘리브레이션 방법을 제안한다. 제안하는 방법은 로봇에 장착된 캐스터 모듈 하나를 고정해 고정된 바퀴를 기준으로 로봇이 회전하는 경우 생기는 기하학적 관계와 로봇의 이동 정보 및 모터 엔코더 정보를 이용해 로봇의 기구학적 파라미터를 구한다. 시뮬레이션과 실제 환경에서 진행된 실험을 통해 제안하는 캘리브레이션 방법을 검증하고 이 방법이 정확한 기구학적 파라미터를 구해 오도메트리 정확도를 향상할 수 있음을 보인다.Kinematic parameters of mobile robot have a great influence on its odometry and control, so many researches were conducted to find accurate kinematic parameters of mobile robot. Since a kinematic calibration method, for finding accurate kinematic parameters, is dependent on the kinematic type of mobile robot, calibration method for certain type is hard to apply for another type. For caster type mobile robots which has complex kinematic model, kinematic parameters are important since inaccurate kinematic parameters cause internal force which results in wheel slippage, a non-systematic error. Previous study on kinematic calibration for caster type mobile robot proposed a method that can only calibrate double-wheeled caster type mobile robot and not single-wheeled caster type mobile robot. This paper proposes a kinematic calibration method for single-wheeled caster type mobile robot. Proposed method uses geometric relationship and movement information of robot and its motor when the robot rotates around its stationary caster wheel. Simulation and hardware experiments conducted in this paper validates the proposed calibration method and shows its performance.제 1 장 서론 1 제 1 절 오도메트리 오차 1 제 2 절 연구 동향 2 제 3 절 연구 기여 5 제 4 절 논문 구성 9 제 2 장 ASOC 기반 모바일 로봇의 캘리브레이션 10 제 1 절 캘리브레이션 방법 10 제 2 절 캘리브레이션 방법의 특징 11 제 3 장 SWPC 기반 모바일 로봇의 캘리브레이션 14 제 1 절 캘리브레이션 방법 14 제 2 절 캘리브레이션 방법의 특징 19 제 4 장 실험 21 제 1 절 시뮬레이션 환경 캘리브레이션 22 제 2 절 실제 환경 캘리브레이션 24 제 3 절 주행 실험 25 제 5 장 결론 33 참고 문헌 35 Abstract 39석

On the expected improvement of odometry estimation in integrated exploration

[EN] The problem of Integrated Exploration is the new trend in the construction of maps of unknown environments; in it, the old paradigm of Simultaneous Localization and Mapping (SLAM) is integrated with the planning of movements necessary for this task to be performed autonomously. However, although motion control is an essential part of this new paradigm, the existing literature has been limited to developing strategies that improve travel times and environmental coverage, leaving aside the impact that these can have on robot odometry and, consequently, on the requirements of localization algorithms. Accordingly, this document presents a new efficient way of exploring environments for the SLAM problem, which aims to improve exploration times and maximize coverage of the work area, as well as minimize the accumulated odometric error to simplify the localization process.[ES] El problema de Exploración integrada es la nueva tendencia en la construcción de mapas de ambientes desconocidos; en ella, se integra el viejo paradigma de la localización y mapeo simultáneos (SLAM) con la planificación de movimientos necesarios, para que esta tarea sea realizada de forma autónoma. Sin embargo, aunque el control de movimientos es una parte esencial de este paradigma, los trabajos encontrados en la literatura se han limitado a desarrollar estrategias que mejoren los tiempos de recorridos y la cobertura del ambiente, dejado de lado el impacto que estos puede tener sobre la odometría del robot y, en consecuencia, sobre los requerimientos de los algoritmos de localización. De lo anterior, en este documento se presenta una nueva forma eficiente de exploración de ambientes para el problema de SLAM, que tiene como objetivo mejorar los tiempos de exploración y maximizar la cobertura del área de trabajo, pero además el de minimizar el error odométrico acumulado para simplificar el proceso de localización.Toriz Palacios, A.; Sánchez López, A. (2020). Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada. Revista Iberoamericana de Automática e Informática industrial. 17(2):229-238. https://doi.org/10.4995/riai.2019.11828OJS229238172Abbas, T., Arif, M., Ahmed, W., 2006. Measurement and correction of systematic odometry errors caused by kinematics imperfections in mobile robots. SICE-ICASE International Joint Conference, 2073-2078. https://doi.org/10.1109/SICE.2006.315554Borenstein, J., 1998. Experimental results from internal odometry error correction with the OmniMate mobile robot. 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IEEE/RSJ International Conference on Intelligent Robots and Systems, 2457-2464. https://doi.org/10.1109/IROS.2006.281689Gil, A., Juliá, M., Reinoso, Ó., 2015. Occupancy grid based graph-SLAM using the distance transform, SURF features and SGD. Engineering Applications of Artificial Intelligence, 40, 1-10. https://doi.org/10.1016/j.engappai.2014.12.010Hähnel, D., Thrun, S., Wegbreit, B., Burgard, W., 2005. Towards lazy data association in SLAM. Eleventh International Symposium Robotics Research, 421-431. https://doi.org/10.1007/11008941_45Hanif, M. S., Bilal, M., Munawar, K., Balamash, A. S., 2018. Implementation of an Embedded Testbed for Indoor SLAM. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), 1-8. https://doi.org/10.1109/AICCSA.2018.8612782Hidalgo-Carrió, J., Hennes, D., Schwendner, J., Kirchner, F., 2017. Gaussian process estimation of odometry errors for localization and mapping. 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Design And Implementation Of An Omnidirectional Mobile Robot Platform

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalısmada robotik alanında yapılan akademik çalısmaların genis bir bölümünde uygulama gelistirme platformu olarak kullanılmak üzere; gerekli islemci gücü, algılama yetileri, hareket kabiliyeti ve iletisim altyapılarını sunan bir mobil robot platform tasarlanmıs ve gerçeklenmistir. Gerçeklenen robotun tabanı, iki diferansiyel sürümlü platformun üzerine sabitlenmistir. Bu sayede serbestlik derecesi dört olan taban, diferansiyel sürümlü platformları kontrol ederek her yöne hareket edebilme yeteneğine sahiptir. Gerçeklenen mekanik tasarımda, odometri tabanlı hassas konumlandırmanın mümkün olabilmesi için, robotun tasarımının kendine has geometrik avantajlarını kullanarak odometri hatalarının azaltılmasına olanak veren bir yöntem sunulmustur. Hareketli platformun üzerindeki donanım bataryalar, üç eksende hareketli bir kamera, çift çekirdekli bir DSP sistemi, Linux tabanlı bir kontrol kartı, kablosuz ağ ve video bağlantısı, grafik LCD ve detayları sunulmus olan, iki eksende hareketli bir lazer isaretçi ile kameranın kullanıldığı, çalısmaya özel olarak gelistirilmis üç boyutlu mesafe ölçerinden olusmaktadır.In this study, an omnidirectional mobile robot with sufficient processing power, sensory units and communication facilities for being utilized as an application development platform for a wide range of academic research in the field of robotics was designed and implemented. The base plane of the robot is attached onto two differential drive platforms, giving four-degrees-of-freedom to the base. This makes the robot able to move to any direction with proper control of the differential drive platforms, giving the property of omnidirectionality. A method to reduce odometric errors and make odometry based accurate positioning possible was also presented which utilizes the geometrical advantages particular to the robot’s mechanic design. The hardware on the moving base consists of batteries, a camera moving in three axes, a dual core DSP system, a Linux based control card, wireless network and video connection, graphical LCD and a laser pointer moving in two axes. An algorithm that uses the laser and the camera to obtain three dimensional distance measurements was also derived.Yüksek LisansM.Sc