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Simulator for Teaching Robotics, ROS and Autonomous Driving in a Competitive Mindset

Simulator for Teaching Robotics, ROS and Autonomous Driving in a Competitive Mindset

Valter Costa, Rosaldo J.F. Rossetti, Armando Sousa
Copyright: © 2019 |Pages: 15
ISBN13: 9781522580607|ISBN10: 1522580603|EISBN13: 9781522580614
DOI: 10.4018/978-1-5225-8060-7.ch033
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MLA

Costa, Valter, et al. "Simulator for Teaching Robotics, ROS and Autonomous Driving in a Competitive Mindset." Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 720-734. https://doi.org/10.4018/978-1-5225-8060-7.ch033

APA

Costa, V., Rossetti, R. J., & Sousa, A. (2019). Simulator for Teaching Robotics, ROS and Autonomous Driving in a Competitive Mindset. In I. Management Association (Ed.), Rapid Automation: Concepts, Methodologies, Tools, and Applications (pp. 720-734). IGI Global. https://doi.org/10.4018/978-1-5225-8060-7.ch033

Chicago

Costa, Valter, Rosaldo J.F. Rossetti, and Armando Sousa. "Simulator for Teaching Robotics, ROS and Autonomous Driving in a Competitive Mindset." In Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 720-734. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8060-7.ch033

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Abstract

Interest in robotics field as a teaching tool to promote the STEM areas has grown in the past years. The search for solutions to promote robotics is a major challenge and the use of real robots always increases costs. An alternative is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and ROS as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. It is intended to take advantage of a competitive mindset to overcome some obstacles that appear to students when designing a real system. The proposed simulator focuses on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests, an accuracy of 99.37% regarding to the semaphore recognition tests, and an average error of 1.8 pixels for the FOV tests performed.

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