Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories

Odometry is a simple and practical method that provides a periodic real-time estimation of the relative displacement of a mobile robot based on the measurement of the angular rotational speed of its wheels. The main disadvantage of odometry is its unbounded accumulation of errors, a factor that reduces the accuracy of the estimation of the absolute position and orientation of a mobile robot. This paper proposes a general procedure to evaluate and correct the systematic odometry errors of a human-sized three-wheeled omnidirectional mobile robot designed as a versatile personal assistant tool. The correction procedure is based on the definition of 36 individual calibration trajectories which together depict a flower-shaped figure, on the measurement of the odometry and ground truth trajectory of each calibration trajectory, and on the application of several strategies to iteratively adjust the effective value of the kinematic parameters of the mobile robot in order to match the estimated final position from these two trajectories. The results have shown an average improvement of 82.14% in the estimation of the final position and orientation of the mobile robot. Therefore, these results can be used for odometry calibration during the manufacturing of human-sized three-wheeled omnidirectional mobile robots

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

Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant

This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory; the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections

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

Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments

New Zealand’s economy relies on primary production to a great extent, where use of the technological advances can have a significant impact on the productivity. Robotics and automation can play a key role in increasing productivity in primary sector, leading to a boost in national economy. This thesis investigates novel methodologies for design, control, and navigation of a mobile robotic platform, aimed for field service applications, specifically in agricultural environments such as orchards to automate the agricultural tasks. The design process of this robotic platform as a non-holonomic omnidirectional mobile robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development and manufacturing of the platform. Robot Operating System (ROS) is employed for the optimum embedded software system design and development to enable control, sensing, and navigation of the platform. 3D modelling and simulation of the robotic system is performed through interfacing ROS and Gazebo simulator, aiming for off-line programming, optimal control system design, and system performance analysis. Gazebo simulator provides 3D simulation of the robotic system, sensors, and control interfaces. It also enables simulation of the world environment, allowing the simulated robot to operate in a modelled environment. The model based controller for kinematic control of the non-holonomic omnidirectional platform is tested and validated through experimental results obtained from the simulated and the physical robot. The challenges of the kinematic model based controller including the mathematical and kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed approach is successfully validated and tested through the simulation and experimental results. Development of a reliable localization system is aimed to enable navigation of the platform in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is considered as the core of the non-GPS localization system. Challenges of SVO are introduced and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is identified in form of rotational and translational drift. Sensor fusion is employed to improve the SVO rotational drift through the integration of IMU and SVO. A novel machine learning approach is proposed to improve the SVO translational drift using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated as a drift estimator for each image frame, then correction is applied at that frame to avoid the accumulation of the drift over time. The experimental results and analyses are presented to validate the effectiveness of the methodology in improving the SVO accuracy. An enhanced SVO is aimed through combination of sensor fusion and machine learning methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a robust non-GPS localization system for the platform, sensor fusion of the wheel odometry and the enhanced SVO is performed to increase the accuracy of the overall system, as well as the robustness of the non-GPS localization system. The experimental results and analyses are conducted to support the methodology

Implementation consensus algorithm and leader-follower of multi-robot system formation

Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.

Implementation consensus algorithm and leader-follower of multi-robot system formation

Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.

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

Towards IMU-based Full-body Motion Estimation of Rough Terrain Mobile Manipulators

For navigation or pose estimation, strap-down Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU) are widely used in all types of mobile devices and applications, from mobile phones to cars and heavy-duty Mobile Working Machines (MWM). This thesis is a summary of work focus on the utilization of IMUs for state estimation of MWM. Inertial sensor-based technology offers an alternative to the traditional solution, since it can significantly decrease the system cost and improve its robustness. For covering the research topic of whole-body estimation with IMUs, five publications focus on the development of novel algorithms, which use sensor fusion or rotary IMU theory to estimate or calculate the states of MWM. The test-platforms are also described in detail. First, we used low-cost IMUs installed on the surface of a hydraulic arm to estimate the joint state. These robotic arms are installed on a floating base, and the joints of the arms rotate in a two-dimensional (2D) plane. The novel algorithm uses an Extended Kalman Filter (EKF) to fuse the output of the gyroscopes and the accelerometers, with gravity as the reference. Second, a rotary gyroscope is mounted on a grasper of a crane, and the rotary gyroscope theory is implemented to decrease the drift of the angular velocity measurement. Third, low-cost IMUs are attached to the wheels and the bogie test bed, and the realization of IMU-based wheel odometry is investigated. Additionally, the rotary gyroscope provides information about the roll and yaw attitude for the test bed. Finally, we used an industry grade IMU fuse with the output of wheel odometry to estimate the position and attitude of the base for an MWM moving on slippery ground. One of the main aims of this research study is to estimate the states of an MWM only using IMU sensors. The research achievements indicate this approach is promising. However, the observability of IMU in the yaw direction of the navigation frame is limited so it is difficult to estimate the yaw angle of the rotation plane for the robotic arm when only using IMUs, to ensure the long-term reliable yaw angle and position of the vehicle base, external information might also be needed. When applying the rotary IMU theory, minimization of the power supply for the rotation device is still a challenge. This research study demonstrates that IMUs can be low-cost and reliable replacements for traditional sensors in joint angle measurement and in the wheel rotation angle for vehicles, among other applications. An IMU can also provide a robust state for a vehicle base in a challenging environment. These achievements will benefit future developments of MWMs in remote control and autonomous operations