Разработка и имплементация сплайн-алгоритма планирования пути в среде ROS/Gazebo

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.Планирование пути для автономных мобильных устройств является важной задачей в робототехнике. При планировании пути принято использовать один из двух классических подходов: глобальный, когда карта полностью известна, и локальный, в котором устройство по мере движения обнаруживает препятствия с помощью различных бортовых датчиков. На основе этих двух подходов также создаются алгоритмы, сочетающие в себе сильные стороны глобального и локального планирования. В ходе предыдущих исследований нами был разработан и реализован в среде Matlab прототип многокритериального сплайн-алгоритма глобального построения маршрута. Алгоритм использует граф Вороного при вычислении первой аппроксимации маршрута для запуска итерационного метода, что позволило находить путь во всех конфигурациях карты при условии существования пути от начальной точки до целевой точки. В ходе итеративного поиска использовалась целевая функция, в которой каждому критерию присваивался его вес в целевой функции. Для реализации критериев в том числе использовался метод потенциальных полей. В данной статье представлена реализация модифицированного сплайн-алгоритма для применения его на реальных автономных мобильных робототехнических системах. Для этого проводится корректирование уравнений характеристических критериев оптимальности пути. Карта препятствий, представленная в ранней версии алгоритма в виде пересечений кругов, в реальных условиях может быть представлена в виде динамически изменяемой вероятностной карты на основе сетки занятости (OccupancyGrid), а робот уже не представляет из себя геометрическую точку. Для реализации сплайн-алгоритма и дальнейшего использования его в системах управления реальных мобильных робототехнических устройств исходный код прототипа алгоритма был перенесен из среды Matlab в модуль программного обеспечения, написанный на языке программирования С++. Тестирование быстродействия алгоритма и оптимальность многокритериальной целевой функции проводились в среде ROS/Gazebo, являющимся на сегодняшний день де-факто стандартом программирования и моделирования робототехнических устройств. Полученный в результате сплайн-алгоритм поиска пути можно интегрировать в системы управления наземных колесных и гусеничных робототехнических устройств, оборудованных лазерным дальномером, а также модифицировать предложенный алгоритм для использования шагающими наземными роботами, беспилотными летающими аппаратами и беспилотными судами. Алгоритм работает в режиме реального времени и параметры влияния критериев на целевую функцию доступны для динамических изменений во время движения мобильного робота

Разработка и имплементация сплайн-алгоритма планирования пути в среде ROS/Gazebo

Планирование пути для автономных мобильных устройств является важной задачей в робототехнике. При планировании пути принято использовать один из двух классических подходов: глобальный, когда карта полностью известна, и локальный, в котором устройство по мере движения обнаруживает препятствия с помощью различных бортовых датчиков. На основе этих двух подходов также создаются алгоритмы, сочетающие в себе сильные стороны глобального и локального планирования. В ходе предыдущих исследований нами был разработан и реализован в среде Matlab прототип многокритериального сплайн-алгоритма глобального построения маршрута. Алгоритм использует граф Вороного при вычислении первой аппроксимации маршрута для запуска итерационного метода, что позволило находить путь во всех конфигурациях карты при условии существования пути от начальной точки до целевой точки. В ходе итеративного поиска использовалась целевая функция, в которой каждому критерию присваивался его вес в целевой функции. Для реализации критериев в том числе использовался метод потенциальных полей. В данной статье представлена реализация модифицированного сплайн-алгоритма для применения его на реальных автономных мобильных робототехнических системах. Для этого проводится корректирование уравнений характеристических критериев оптимальности пути. Карта препятствий, представленная в ранней версии алгоритма в виде пересечений кругов, в реальных условиях может быть представлена в виде динамически изменяемой вероятностной карты на основе сетки занятости (OccupancyGrid), а робот уже не представляет из себя геометрическую точку. Для реализации сплайн-алгоритма и дальнейшего использования его в системах управления реальных мобильных робототехнических устройств исходный код прототипа алгоритма был перенесен из среды Matlab в модуль программного обеспечения, написанный на языке программирования С++. Тестирование быстродействия алгоритма и оптимальность многокритериальной целевой функции проводились в среде ROS/Gazebo, являющимся на сегодняшний день де-факто стандартом программирования и моделирования робототехнических устройств. Полученный в результате сплайн-алгоритм поиска пути можно интегрировать в системы управления наземных колесных и гусеничных робототехнических устройств, оборудованных лазерным дальномером, а также модифицировать предложенный алгоритм для использования шагающими наземными роботами, беспилотными летающими аппаратами и беспилотными судами. Алгоритм работает в режиме реального времени и параметры влияния критериев на целевую функцию доступны для динамических изменений во время движения мобильного робота

GEMA2:Geometrical matching analytical algorithm for fast mobile robots global self-localization

[EN] This paper presents a new algorithm for fast mobile robot self-localization in structured indoor environments based on geometrical and analytical matching, GEMA(2). The proposed method takes advantage of the available structural information to perform a geometrical matching with the environment information provided by measurements collected by a laser range finder. In contrast to other global self-localization algorithms like Monte Carlo or SLAM, GEMA(2) provides a linear cost with respect the number of measures collected, making it suitable for resource-constrained embedded systems. The proposed approach has been implemented and tested in a mobile robot with limited computational resources showing a fast converge from global self-localization. (C) 2014 Elsevier B.V. All rights reserved.This work has been partially funded by FEDER-CICYT projects with references DPI2011-28507-C02-01 and HAR2012-38391-C02-02 financed by Ministerio de Ciencia e Innovacion and Ministerio de Economia y Competitividad (Spain).Sánchez Belenguer, C.; Soriano Vigueras, Á.; Vallés Miquel, M.; Vendrell Vidal, E.; Valera Fernández, Á. (2014). GEMA2:Geometrical matching analytical algorithm for fast mobile robots global self-localization. Robotics and Autonomous Systems. 62(6):855-863. https://doi.org/10.1016/j.robot.2014.01.009S85586362

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

Image-guided Landmark-based Localization and Mapping with LiDAR

Mobile robots must be able to determine their position to operate effectively in diverse environments. The presented work proposes a system that integrates LiDAR and camera sensors and utilizes the YOLO object detection model to identify objects in the robot's surroundings. The system, developed in ROS, groups detected objects into triangles, utilizing them as landmarks to determine the robot's position. A triangulation algorithm is employed to obtain the robot's position, which generates a set of nonlinear equations that are solved using the Levenberg-Marquardt algorithm. The presented work comprehensively discusses the proposed system's study, design, and implementation. The investigation begins with an overview of current SLAM techniques. Next, the system design considers the requirements for localization and mapping tasks and an analysis comparing the proposed approach to the contemporary SLAM methods. Finally, we evaluate the system's effectiveness and accuracy through experimentation in the Gazebo simulation environment, which allows for controlling various disturbances that a real scenario can introduce

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

A New Three Object Triangulation Algorithm for Mobile Robot Positioning

Positioning is a fundamental issue in mobile robot applications. It can be achieved in many ways. Among them, triangulation based on angles measured with the help of beacons is a proven technique. Most of the many triangulation algorithms proposed so far have major limitations. For example, some of them need a particular beacon ordering, have blind spots, or only work within the triangle defined by the three beacons. More reliable methods exist; however, they have an increasing complexity or they require to handle certain spatial arrangements separately. In this paper, we present a simple and new three object triangulation algorithm, named ToTal, that natively works in the whole plane, and for any beacon ordering. We also provide a comprehensive comparison between many algorithms, and show that our algorithm is faster and simpler than comparable algorithms. In addition to its inherent efficiency, our algorithm provides a very useful and unique reliability measure, assessable anywhere in the plane, which can be used to identify pathological cases, or as a validation gate in Kalman filters.Peer reviewe

Vision-Based Mobile Robot Self-localization and Mapping System for Indoor Environment

Localizing accurately and building map of an environment concurrently is a key factor of a mobile robot system. In this system, the robot makes localization and mapping with artificial landmarks and map-based system. It is a process by which a mobile robot can build a map of an environment while continuously determining the location of the robot within the map. The system estimates the robot position in indoor environments using sensors; a camera, three ultrasonic sensors and encoders. The main contribution of this paper is to reduce computational time and improve mapping with map-based system. The self-localization of mobile robot in an indoor environment is advanced through the construction of map based on sensors and recognition of artificial landmarks. Vision based localization system can benefit from using with ultrasonic sensors. From captured images, the system makes landmark detection by using Canny edge detection and Chain-code Approximation algorithms to represent the contour of landmarks by using edge points. The Kalman filter is aimed to accurately estimate position and orientation of the robot using relative distances to walls or artificial landmarks in environments. A robotic system is capable of mapping in an indoor environment and localizing with respect to the map, in real time, using artificial landmarks and sensors

Low cost inertial-based localization system for a service robot

Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science EngineeringThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation