Extended Kalman Filter Implementation for the Khepera II Mobile Robot

The accurate estimation of robot position and orientation in real-time is one of the fundamental challenges in mobile robotics. The Extended Kalman Filter is a nonlinear real-time recursive time domain filter that combines available sensor data to produce an accurate estimate of state, and has been successfully applied to the localization problem in mobile robotics and aircraft navigation. This report describes an Extended Kalman Filter implementa- tion for the Khepera II mobile robotics platform that seeks to produce accurate localization estimates in real-time using wheel odometry data, IR sensor range data, and compass heading data

Online Mapping-Based Navigation System for Wheeled Mobile Robot in Road Following and Roundabout

A road mapping and feature extraction for mobile robot navigation in road roundabout and road following environments is presented in this chapter. In this work, the online mapping of mobile robot employing the utilization of sensor fusion technique is used to extract the road characteristics that will be used with path planning algorithm to enable the robot to move from a certain start position to predetermined goal, such as road curbs, road borders, and roundabout. The sensor fusion is performed using many sensors, namely, laser range finder, camera, and odometry, which are combined on a new wheeled mobile robot prototype to determine the best optimum path of the robot and localize it within its environments. The local maps are developed using an image’s preprocessing and processing algorithms and an artificial threshold of LRF signal processing to recognize the road environment parameters such as road curbs, width, and roundabout. The path planning in the road environments is accomplished using a novel approach so called Laser Simulator to find the trajectory in the local maps developed by sensor fusion. Results show the capability of the wheeled mobile robot to effectively recognize the road environments, build a local mapping, and find the path in both road following and roundabout

Implementation of the autonomous functionalities on an electric vehicle platform for research and education

Self-driving cars have recently captured the attention of researchers and car manufacturing markets. Depending upon the level of autonomy, the cars are made capable of traversing from one point to another autonomously. In order to achieve this, sophisticated sensors need to be utilized. A complex set of algorithms is required to use the sensors data in order to navigate the vehicle along the desired trajectory. Polaris is an electric vehicle platform provided for research and education purposes at Aalto University. The primary focus of the thesis was to utilize all the sensors provided in Polaris to their full potential. So that, essential data from each sensor is made available to be further utilized either by a specific automation algorithm or by some mapping routine. For any autonomous robotic system, the first step towards automation is localization. That is to determine the current position of the robot in a given environment. Different sensors mounted over the platform provide such measurements in different frames of reference. The thesis utilizes the GPS based localization solution combined with the LiDAR data and wheel odometry to perform autonomous tasks. Robot Operating System is used as the software development tool in thesis work. Autonomous tasks include the determination of the global as well as the local trajectories. The endpoints of the global trajectories are dictated by the set of predefined GPS waypoints. This is called target-point navigation. A path needs to be planned that avoids all the obstacles. Based on the planned path, a set of velocity commands are issued by the embedded controller. The velocity commands are then fed to the actuators to move the vehicle along the planned trajectory

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

Robot Mapping and Localisation in Water Pipes

The demand for inspection and repair technologies for the water industries on their water mains and distribution pipes is increasing. In urban water distribution systems, due to the fact that water pipes are ageing, pipe leakages and serious damage may occur. Compared with the cost of pipe replacement in the underground distribution system, regular pipe inspection and repair is more cost effective to water companies and local communities. However, small-diameter pipes are not accessible to humans because they are small in size and often buried underground. Therefore, inspection robotic systems are more suited to this task in terms of underground pipe networks mapping and damage localisation, in order to target repair from above ground. There are a number of challenges for robot mapping and localisation in water pipes, which are: 1) feature sparsity in water pipes – lack of navigation landmarks, 2) in-pipe robot can only detect nearby features, and 3) unlike indoor/outdoor SLAM problems, in-pipe robot has less movement flexibility. The main aim of this thesis is to solve these challenges and address the problem of robot mapping and localisation in small-diameter feature-sparse water pipes. This thesis presents a number of novel contributions. Firstly, for the front end, where raw sensor data is transformed into signals useful for mapping and localisation algorithms, new types of maps are developed here for water pipes: for plastic pipes, ultrasound data is used to map the ground profile through the plastic pipe wall, whilst for metal pipes a hydrophone is used to determine a map based on pipe vibration amplitude over space. Secondly, a new sequential approach to mapping and localisation is developed, based on alignment of multiple maps based on dynamic time warping averaging. Thirdly, a new simultaneous localisation and mapping algorithm is developed, which overcomes the limitation of the sequential approach in that the map is not updated. Finally, a new sensor fusion algorithm is developed that transforms the robot location in the local coordinate frame to the world coordinate frame, which would be essential for targeting repairs from above ground

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

CONVERGENT POSE AND TWIST ESTIMATION FOR VELOCITY-DENIED MOBILE ROBOTS BASED ON A CASCADING, DUAL-FRAME, MOTION-TRACKING ESTIMATOR

Ph.D

Autonomous mobile robot navigation using fuzzy logic control

Traditionally the type of robot used in the workplace consisted mainly o f the fixed arm variety. Any mobile robots that were commercially available required that the environment be altered to accommodate them. This involved the installation of guide lanes or some form of sensor units placed at various locations around the workplace to facilitate the robot in determining its position within the environment. Such approaches are costly and limit the use of robots to environments where these methods are feasible. The inadequacies in this technology has led to research into autonomous mobile robots that offer greater flexibility and do not require changes in the enviromnent. There are many technical issues to be addressed in designing such a robot. These stem from the necessity that the robot must be able to navigate through an environment unaided. Other problems such as the cost of the vehicle must be considered so that prospective customers will not be put off. This thesis discusses the strategies taken in addressing the problems associated with navigation in an obstacle strewn environment. Such issues include position estimation, path planning, obstacle avoidance and the acquisition and interpretation of sensor information. It also discusses the suitability of fuzzy logic for controlling a robot. A graphical user interface runs on the PC which communicates with the robot over a radio link. The robot uses a fuzzy logic controller to follow a planned path and avoid unknown obstacles by controlling the velocity and steering angle o f the drive unit. It is a tracked vehicle which is suitable for indoor use only. The results of path planning and the robots attempts at following the paths and avoiding obstacles are illustrated and discussed

Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

[EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072TESI