Design and development of software stack of an autonomous vehicle using robot operating system

Prasad, Abhisek Omkar; Mishra, Pradumn; Jain, Urja; Pandey, Anish; Sinha, Anushka; Yadav, Anil Singh; Kumar, Rajan; Sharma, Abhishek; Kumar, Gaurav; Salem, Karrar hazim; Sharma, Aneesh; Dixit, Anil Kumar

doi:10.1016/j.robot.2022.104340

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…Hardware components, including sensors and cameras, are explored for their vital role in path tracking, computer vision-based control, object avoidance, and real-time object differentiation through filters, highlighting their significance in car control. [2] Gautam et al proposed system aims to enhance autonomous vehicle safety through an Automatic Traffic Light Detection System (ALTDS). Tailored for precise traffic light detection in both autonomous vehicles and Driver Assistance Systems (DAS), this vision-based system employs a camera as the primary sensor, eliminating the need for additional configurations.…”

Section: Literature Surveymentioning

confidence: 99%

Urban Traffic Detection for Autonomous Vehicles

Karle,

Awaish,

Singh

et al. 2024

AJCT

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Autonomous vehicles leverage advanced sensors, artificial intelligence, and automation, enabling selfnavigation without human intervention. These vehicles hold the potential to significantly improve road safety, enhance efficiency, and revolutionize transportation systems by reshaping how vehicles perceive, interpret, and respond to their environment. The demand for such vehicles arises from the desire for improved urban planning, decreased parking needs, and flexible public transportation. Automation reduces errors, optimizes traffic flow, and produces favorable economic results.This study underscores the crucial importance of advanced traffic and lane detection in reinforcing the reliability and safety of autonomous vehicles, playing a pivotal role in their ongoing evolution.The proposed system operates in real-time, employing dynamic traffic data to inform decision-making. It integrates inputs from cameras, processing parameters such as lane positions, obstacles, and traffic symbols. A centralized control system, comprising Raspberry Pi and Arduino as master-slave components, employs specialized models for lane, object, and traffic symbol detection. This architecture guarantees continuous real-time decisionmaking and optimizes resource allocation, promoting a resilient and adaptive autonomous driving paradigm. The comprehensive nature of this approach not only aligns with contemporary transportation requirements but also proactively tackles the challenges anticipated in the future urban mobility landscape.

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Section: Literature Surveymentioning

confidence: 99%

Urban Traffic Detection for Autonomous Vehicles

Karle,

Awaish,

Singh

et al. 2024

AJCT

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“…Additionally, consider leveraging existing ROS packages, libraries, and tools that provide functionalities for sensor integration, actuator control, and planning and control algorithms. The ROS ecosystem offers numerous resources that can accelerate development and provide a solid foundation for autonomous vehicle software stacks [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Low-Cost Small Scale Autonomous Vehicle

Bogrekci,

Demircioglu,

Goren

2024

JAMRIS

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A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is affordable and designed for smaller applications or purposes. The firefly algorithm was used to solve obstacle-avoiding problems due to dynamic or static uncertain positioned obstacles. The autonomous vehicle reached the determined position, which could be considered accurate. Regardless of the starting point, it arrived at the defined destination in an area with a diameter of 5 meters. Considering the costs of the sensors, simple algorithm, not very powerful processor and circuit elements, sufficient success has been achieved.

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“…Gazebo allows for testing robotic models in virtual environments and incorporating sensor data [24]. It enables testing the functionality of a robotic model under particular circumstances without causing any damage to the hardware [25]. It is powered by a physical mechanism that generates light, inertia, and gravity.…”

Section: Simulation Environmentmentioning

confidence: 99%

Simulation of autonomous navigation of turtlebot robot system based on robot operating system

Ghazal,

Al-Ghadhanfari,

Waisi

2024

Bulletin EEI

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Complex system science has recently shifted its focus to include modeling, simulation, and behavior control. An effective simulation software built on robot operating system (ROS) is used in robotics development to facilitate the smooth transition between the simulation environment and the hardware testing of control behavior. In this paper, we demonstrate how the simultaneous localization and mapping (SLAM) algorithm can be used to allow a robot to navigate autonomously. The Gazebo is used to simulate the robot, and Rviz is used to visualize the simulated data. The G-mapping package is used to create maps using collected data from a variety of sensors, including laser and odometry. To test and implement autonomous navigation, a Turtlebot was used in a Gazebo-generated simulated environment. In our opinion, additional study on ROS using these important tools might lead to a greater adoption of robotics tests performed, further evaluation automation, and efficient robotic systems.

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Design and development of software stack of an autonomous vehicle using robot operating system

Cited by 19 publications

References 15 publications

Urban Traffic Detection for Autonomous Vehicles

Urban Traffic Detection for Autonomous Vehicles

Low-Cost Small Scale Autonomous Vehicle

Simulation of autonomous navigation of turtlebot robot system based on robot operating system

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