Dynamic Simulation and Kinematic Control for Autonomous Driving in Automobile Robots
This article proposes a simple simulation methodology that allows to experiment with the dynamic behavior of vehicles, which applies control laws that allow automated driving in unstructured environments, through the use of robotic application simulation
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, Bryan S. Guevara2 , Luis F. Recalde2 and Víctor H. Andaluz2
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1 Escuela Superior Politécnica de Chimborazo, Riobamba, Ecuador
[email protected] 2 Universidad de la Fuerzas Armadas ESPE, Sangolquí, Ecuador
{bsguevara,lfrecalde1,vhandaluz1}@espe.edu.ec
Abstract. This article proposes a simple simulation methodology that allows to experiment with the dynamic behavior of vehicles, which applies control laws that allow automated driving in unstructured environments, through the use of robotic application simulation software Webots, which shortens the experimentation times of autonomous driving systems, It also allows to overcome the limitations that this type of projects involves, such as, high infrastructure costs, the difficulties of validating the system, qualified personnel and the participation of various elements with different dynamic behaviors that make up a real traffic space. The use of the virtualized model of a BMW X5 vehicle and the instrumentation of multiple sensors necessary for its operation are presented. Finally, a path tracking algorithm is developed using Matlab scientific programming software, and the dynamic behavior of the vehicle is evaluated through the response curves of the robot states, these results represent the starting point for future research and the implementation of physical tests. Keywords: Autonomous driving · Car-Like · Dynamic simulation · Kinematic control · Robotic
1 Introduction Since the 1950s, research on automated driving systems began with the aim of solving traffic problems, so autonomous car navigation has been a subject of great interest to the scientific community and the automotive industry [1]. Autonomous driving systems can be classified as autonomous and collaborative. Autonomous systems are characterized by the ability to provide a certain type of intelligence on board the vehicle, in this type of system, sensors that warn of the state of the environment are used, such as GPS, digital road maps, artificial vision and among others, without the need to use any kind of special equipment on the road. On the other hand, the cooperative system is defined as a synergistic system between installed road intelligence, such as inductive cables and magnetic markers and intelligence on board [2]. Another definition provides the
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Botto-Tobar et al. (Eds.): ICAETT 2020, AISC 1302, pp. 205–216, 2021. https://doi.org/10.1007/978-3-030-63665-4_16
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scope of automation [3]: a) Automated driving, enhanced driving by dedicated control and autonomous systems that support driving, taking into account that control can be recovered at all times and that the driver is legally responsible to carry out the driving task; and b) Autonomous driving: makes mention of the end result of driving automation, in principle no human driver needs to be active in the operation of the vehicle, although a driver may still be, but need not be on site. In today’s cars, electronics plays an
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