Управление на колесен мобилен робот при следене на зададена траектория

В статията са изследвани проблемите на устойчивост при движение на четири-колесен мобилен робот по зададена траектория. Обект на изследването е колесен мобилен робот с предни завиващи и задни задвижващи колела по схема Акерман. Направена е симулация, базирана на bicycle математичен модел, в която се изчисляват надлъжната и напречната устойчивост. От симулацията се вижда че ускоренията не надвишават пределните стойности гарантиращи устойчивостта на робота по време на движението по зададената траектория.In the article, the problems of stability during movement of a four-wheeled mobile robot along a set trajectory are investigated. The object of the study is a wheeled mobile robot with front turning and rear driving wheels according to the Ackerman scheme. A simulation was made based on a bicycle mathematical model, in which the longitudinal and transverse stability are calculated. The simulation shows that the accelerations do not exceed the limit values guaranteeing the stability of the robot during the movement along the set trajectory

Моделиране на устойчивостта на колесни мобилни роботи по крен и тангаж

This article discusses the stability of wheeled mobile robots on roll and pitch, depending on their geometric proportions, as well as the forces acting on them. A dynamic model of this type of robot is built, based on the principles of kinetostatic. The influence of the parameters of the model on the roll and pitch stability has been studied. Coefficients of stability have been introduced. The dynamic model is built in such a way that it is possible to determine intervals with values of the parameters at which roll and pitch stability is guaranteed

Изследване на влиянието на параметрите на движението върху к.п.д. на правотоков двигател за колесен мобилен робот

The thematic is in the engineering (mechatronics) branch, with application in the mobile robotics. In the article have been examined: the mechanical energy at motion and its corresponding electrical consumption, as they define the energy effectiveness for the mechatronics system of a vehicle. There have been analysed dependencies, defined by the physics, in order to find out the demands, imposed on the construction "battery, electrical motor, gearing and drive wheels". The goal criterion is the achieved mileage with a single battery charge, which is determined by the dependencies "electrical consumption versus torque versus speed" at any actual motor type. The robot's dynamics parameters (weight, friction at movement, terrain slope, maximal speed and acceleration) appear as input data. Combinations between model parameters have been made, in order to be determined the optimal energy indicators as well as to choose the appropriate direct- current motor. This research uses a relatively small set of empirical physics coefficients and it can be used as an effortless methodology in some lecture notes on the mechatronics of a small wheeled mobile robot

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles

Rapid advances in every sphere of autonomous driving technology have intensified the need to be able to benchmark and compare different approaches. While many benchmarking tools tailored to different sub-systems of an autonomous vehicle, such as perception, already exist, certain aspects of autonomous driving still lack the necessary depth and diversity of coverage in suitable benchmarking approaches - autonomous vehicle motion planning is one such aspect. While motion planning benchmarking tools are abundant in the robotics community in general, they largely tend to lack the specificity and scope required to rigorously compare algorithms that are tailored to the autonomous vehicle domain. Furthermore, approaches that are targeted at autonomous vehicle motion planning are generally either not sensitive enough to distinguish subtle differences between different approaches, or not able to scale across problems and operational design domains of varying complexity. This work aims to address these issues by proposing WiseBench, an autonomous vehicle motion planning benchmark framework aimed at comprehensively uncovering fine and coarse-grained differences in motion planners across a wide range of operational design domains. WiseBench outlines a robust set of requirements for a suitable autonomous vehicle motion planner. These include simulation requirements that determine the environmental representation and physics models used by the simulator, scenario-suite requirements that govern the type and complexity of interactions with the environment and other traffic agents, and comparison metrics requirements that are geared towards distinguishing the behavioral capabilities and decision making processes of different motion planners. WiseBench is implemented using a carefully crafted set of scenarios and robust comparison metrics that operate within an in-house simulation environment, all of which satisfy these requirements. The benchmark proved to be successful in comparing and contrasting two different autonomous vehicle motion planners, and was shown to be an effective measure of passenger comfort and safety in a real-life experiment. The main contributions of our work on WiseBench thus include: a scenario creation methodology for the representative scenario suite, a comparison methodology to evaluate different motion planning algorithms, and a proof-of-concept implementation of the WiseBench framework as a whole