Encoderless position estimation and error correction techniques for miniature mobile robots

This paper presents an encoderless position estimation technique for miniature-sized mobile robots. Odometry techniques, which are based on the hardware components, are commonly used for calculating the geometric location of mobile robots. Therefore, the robot must be equipped with an appropriate sensor to measure the motion. However, due to the hardware limitations of some robots, employing extra hardware is impossible. On the other hand, in swarm robotic research, which uses a large number of mobile robots, equipping the robots with motion sensors might be costly. In this study, the trajectory of the robot is divided into several small displacements over short spans of time. Therefore, the position of the robot is calculated within a short period, using the speed equations of the robot's wheel. In addition, an error correction function is proposed that estimates the errors of the motion using a current monitoring technique. The experiments illustrate the feasibility of the proposed position estimation and error correction techniques to be used in miniature-sized mobile robots without requiring an additional sensor

Location of a Mobile Robot using Odometry in the DMF

La Universidad de Ciencias Aplicadas de Viena, cuenta con una fábrica digital en miniatura en la que se puede realizar la investigación, desarrollo e implementación de las diferentes tecnologías de la industria 4.0. Esta fábrica tiene varias estaciones de trabajo y un robot móvil que se mueve entre ellas para hacer llegar al cliente las piezas pedidas correspondientes del mosquetón. El tema principal de este trabajo fin de grado es el desarrollo de un procedimiento por el cual se pueda obtener la localización del robot calculando sus coordenadas y su ángulo; todo ello con el objetivo de integrarlo en la fábrica miniaturizada de la universidad. El método que se usará para conocer la posición y orientación del robot estará basado en la odometría de un robot diferencial. El control del robot se realizará mediante el puerto serie de Arduino o mediante Thing Worx, enviando los comandos necesarios para su movimiento. La pose (posición en coordenadas y orientación) del robot será enviada al servidor central haciendo uso de la comunicación IoT, donde se podrán visualizar y hacer uso para otros trabajos.The University of Applied Sciences Technikum Wien has a digital miniature factory in which it can be done the research, development and implementation of different technologies related with the 4.0 industry. This miniature factory has several working stations and a mobile robot that moves between them in order to deliver the corresponding carabiner parts ordered by the supposed customer. The main subject of this final bachelor project is the development of a procedure by which the localization of the mobile robot can be obtained by calculating its coordinates and angle; all with the aim of integrating it into the miniaturised factory of the university. The method to be used to know the position and orientation of the robot will be based on the wheel odometry of a differential robot. The control of the robot is done through the serial port of the Arduino or through ThingWorx, sending the necessary commands to make it moves. The pose (position in coordinates and orientation) of the robot will be sent to the central server using IoT communication, where it can be visualised and used for other projects.Departamento de Tecnología ElectrónicaGrado en Ingeniería en Electrónica Industrial y Automátic

Event based localization in Ackermann steering limited resource mobile robots

“© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”This paper presents a local sensor fusion technique with an event-based global position correction to improve the localization of a mobile robot with limited computational resources. The proposed algorithms use a modified Kalman filter and a new local dynamic model of an Ackermann steering mobile robot. It has a similar performance but faster execution when compared to more complex fusion schemes, allowing its implementation inside the robot. As a global sensor, an event-based position correction is implemented using the Kalman filter error covariance and the position measurement obtained from a zenithal camera. The solution is tested during a long walk with different trajectories using a LEGO Mindstorm NXT robot.This work was supported by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by the Ministerio de Ciencia e Innovacion (Spain). This work was also supported by the University of Costa Rica.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2014). Event based localization in Ackermann steering limited resource mobile robots. IEEE/ASME Transactions on Mechatronics. 19(4):1171-1182. doi:10.1109/TMECH.2013.2277271S1171118219

Efficient calibration of four wheel industrial AGVs

In this paper, we propose a novel method for extrinsic and intrinsic automatic calibration of four wheel industrial Automated Guided Vehicles (AGVs) compliant with Ackermann and Dual Drive kinematics. For each kinematic model the algorithm estimates the trajectories measured by an on-board sensor and the expected ones given the state of the wheels. The estimation exploits the model equations derived in this work which constrain calibration parameters and measurements from wheel encoders and sensor odometry. The parameter values are computed through closed-form solutions of least-square estimation. The method has been implemented on Programmable Logic Controllers and tested on industrial AGVs. The developed procedure computes the parameters in about 10−15 minutes, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in a warehouse have assessed the accuracy and stability of the proposed approach. The position accuracy achieved by AGVs calibrated with the proposed method is higher than the one achieved by manual calibration

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

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño

Map-based localization for urban service mobile robotics

Mobile robotics research is currently interested on exporting autonomous navigation results achieved in indoor environments, to more challenging environments, such as, for instance, urban pedestrian areas. Developing mobile robots with autonomous navigation capabilities in such urban environments supposes a basic requirement for a upperlevel service set that could be provided to an users community. However, exporting indoor techniques to outdoor urban pedestrian scenarios is not evident due to the larger size of the environment, the dynamism of the scene due to pedestrians and other moving obstacles, the sunlight conditions, and the high presence of three dimensional elements such as ramps, steps, curbs or holes. Moreover, GPS-based mobile robot localization has demonstrated insufficient performance for robust long-term navigation in urban environments. One of the key modules within autonomous navigation is localization. If localization supposes an a priori map, even if it is not a complete model of the environment, localization is called map-based. This assumption is realistic since current trends of city councils are on building precise maps of their cities, specially of the most interesting places such as city downtowns. Having robots localized within a map allows for a high-level planning and monitoring, so that robots can achieve goal points expressed on the map, by following in a deliberative way a previously planned route. This thesis deals with the mobile robot map-based localization issue in urban pedestrian areas. The thesis approach uses the particle filter algorithm, a well-known and widely used probabilistic and recursive method for data fusion and state estimation. The main contributions of the thesis are divided on four aspects: (1) long-term experiments of mobile robot 2D and 3D position tracking in real urban pedestrian scenarios within a full autonomous navigation framework, (2) developing a fast and accurate technique to compute on-line range observation models in 3D environments, a basic step required by the real-time performance of the developed particle filter, (3) formulation of a particle filter that integrates asynchronous data streams and (4) a theoretical proposal to solve the global localization problem in an active and cooperative way, defining cooperation as either information sharing among the robots or planning joint actions to solve a common goal.Actualment, la recerca en robòtica mòbil té un interés creixent en exportar els resultats de navegació autònoma aconseguits en entorns interiors cap a d'altres tipus d'entorns més exigents, com, per exemple, les àrees urbanes peatonals. Desenvolupar capacitats de navegació autònoma en aquests entorns urbans és un requisit bàsic per poder proporcionar un conjunt de serveis de més alt nivell a una comunitat d'usuaris. Malgrat tot, exportar les tècniques d'interiors cap a entorns exteriors peatonals no és evident, a causa de la major dimensió de l'entorn, del dinamisme de l'escena provocada pels peatons i per altres obstacles en moviment, de la resposta de certs sensors a la il.luminació natural, i de la constant presència d'elements tridimensionals tals com rampes, escales, voreres o forats. D'altra banda, la localització de robots mòbils basada en GPS ha demostrat uns resultats insuficients de cara a una navegació robusta i de llarga durada en entorns urbans. Una de les peces clau en la navegació autònoma és la localització. En el cas que la localització consideri un mapa conegut a priori, encara que no sigui un model complet de l'entorn, parlem d'una localització basada en un mapa. Aquesta assumpció és realista ja que la tendència actual de les administracions locals és de construir mapes precisos de les ciutats, especialment dels llocs d'interés tals com les zones més cèntriques. El fet de tenir els robots localitzats en un mapa permet una planificació i una monitorització d'alt nivell, i així els robots poden arribar a destinacions indicades sobre el mapa, tot seguint de forma deliberativa una ruta prèviament planificada. Aquesta tesi tracta el tema de la localització de robots mòbils, basada en un mapa i per entorns urbans peatonals. La proposta de la tesi utilitza el filtre de partícules, un mètode probabilístic i recursiu, ben conegut i àmpliament utilitzat per la fusió de dades i l'estimació d'estats. Les principals contribucions de la tesi queden dividides en quatre aspectes: (1) experimentació de llarga durada del seguiment de la posició, tant en 2D com en 3D, d'un robot mòbil en entorns urbans reals, en el context de la navegació autònoma, (2) desenvolupament d'una tècnica ràpida i precisa per calcular en temps d'execució els models d'observació de distàncies en entorns 3D, un requisit bàsic pel rendiment del filtre de partícules a temps real, (3) formulació d'un filtre de partícules que integra conjunts de dades asíncrones i (4) proposta teòrica per solucionar la localització global d'una manera activa i cooperativa, entenent la cooperació com el fet de compartir informació, o bé com el de planificar accions conjuntes per solucionar un objectiu comú