Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter

Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIMwheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter

Mobile Robot Localization Based on Kalman Filter

Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system

Autonomous location system based on propioceptive sensor fusion for mobile robots

El objetivo principal de este trabajo es desarrollar un sistema de localización autónomo capaz de entregar mejores estimaciones de posición en comparación a un sistema exclusivamente odométrico mediante un algoritmo de fusión sensorial. Un robot móvil recorre una trayectoria previamente programada para proporcionar datos sensoriales al sistema. Se define una arquitectura de fusión que trabaja con datos de odómetros, acelerómetros y giroscopio. El modelo de movimiento del robot, el modelo de medición y los datos sensoriales se fusionan empleando un filtro de Kalman extendido. Los resultados muestran que en todos los casos evaluados, el sistema registra una mejora del 38% en comparación a un sistema de localización determinístico estándar. Por otra parte, los datos revelan que la variable θ es la más influyente en el proceso. En conclusión, los resultados satisfacen el objetivo planteado, sin embargo, pueden ser mejorados incorporando sensores adicionales y ajustando las matrices de incertidumbre R y Q.The main objective of this study is to develop an autonomous localization system capable of delivering better position estimates compared to an exclusively odometer system by means of a sensor fusion algorithm. A mobile robot travels a pre-programmed path to provide sensory data to the system. A fusion architecture is define that works with odometers, accelerometers and gyroscope data. The robot movement model, the measurement model and the sensory data are using an Extended Kalman Filter. The results show that in all the cases that were evaluated the system records an improvement of 38% compared to a standard deterministic localization system. The data show that the θ variable is the most influential in the process. In conclusion, the results satisfy the stated objective, nevertheless, it can be improved by incorporating additional sensors and adjusting the uncertainty matrices R and Q

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

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

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

Estudo e desenvolvimento de um sistema de autolocalização para um veículo autónomo

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresO desenvolvimento de sistemas de localização robustos, eficientes e de baixo custo permanece como uma das questões importantes dos veículos autónomos. Esta dissertação foi desenvolvida em parceria com o CEiiA e tem como principal objetivo o desenvolvimento de um sistema de autolocalização de baixo custo para um veículo autónomo que circulará no exterior. O cumprimento deste objetivo envolveu a especificação, implementação e teste de um protótipo. Envolveu também o desenvolvimento do firmware do microcontrolador (C/C++) para realizar a recolha e comunicação dos dados recolhidos pelos diversos sensores. A plataforma móvel utilizada para testes foi uma bicicleta na qual se integraram um módulo GPS, um encoder absoluto, um alternador (utilizado como um encoder incremental), um giroscópio, um acelerómetro e um magnetómetro. Os dados são adquiridos a uma frequência de 40 Hz e guardados num cartão μSD ou transmitidos através de um módulo de comunicação sem fios XBee. Para realizar a fusão sensorial exploraram-se o filtro complementar, o filtro de Kalman Extended e o filtro de Kalman Unscented, através do software Matlab. De forma a permitir a visualização dos resultados, desenvolveu-se uma interface gráfica em HTML, CSS e Javascript. Realizaram-se diversos testes ao sistema, tendo-se obtido na medição da posição do veículo um erro máximo de 10 m por parte do GPS. Foi também verificado que o erro da odometria é proporcional ao aumento da complexidade da trajetória e da distância percorrida. De entre os três filtros aplicados, o filtro complementar revelou ser uma solução satisfatória para percursos simples. Não houve diferenças significativas entre os resultados obtidos com os diferentes filtros de Kalman.The development of robust, efficient and low-cost localization systems remains one of the important issues of autonomous vehicles. This dissertation was developed in partnership with CEiiA and its main objective is the development of a low-cost self-localization system for an autonomous vehicle that will circulate in a non-controlled environment. The fulfillment of this objective involved the specification, implementation and testing of a prototype. It also involved the development of microcontroller firmware (C/C++) to gather and communicate the data collected by the various sensors. The mobile platform used for testing was a bicycle in which a GPS module, an absolute encoder, an alternator (used as an incremental encoder), a gyroscope, an accelerometer and a magnetometer were integrated. The data is acquired at a frequency of 40Hz and stored on a μSD card or transmitted through an XBee wireless communication module. The Complementary filter, the Extended Kalman filter and the Unscented Kalman filter were explored through Matlab software. In order to allow the visualization of the results, a graphical user interface was developed in HTML, CSS and Javascript. Several tests were performed on the system, and a maximum error of 10 m was obtained by the GPS in the vehicle position measurement. It was also verified that the error of the odometry is proportional to the increase of the complexity and distance covered. Of the three filters applied, the complimentary filter proved to be a satisfactory solution for simple routes. There was no significant differences between the results obtained with the different Kalman filters