Integration of Drive-by-Wire with Navigation Control for a Driverless Electric Race Car

Drage, Thomas; Kalinowski, Jordan; Bräunl, Thomas

doi:10.1109/mits.2014.2327160

Cited by 24 publications

(8 citation statements)

References 20 publications

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“…To localize the car we developed an Extended Kalman Filter (EKF) based on the single track model of Equations (1), (6), (7), that receives as inputs the high rate measure updates from the vehicle sensors (IMU, wheel speed and optical speed sensor) as well as a low rate update from the LIDAR+IMU+GPS odometry. In this way it is easy to compute frequent updates of the vehicle state while correcting drift using LIDAR sensors input.…”

Section: Mapping and Localizationmentioning

confidence: 99%

“…In this work, we focus on the particular scenario of a racing track environment, which allows driverless vehicles testing in extreme conditions (high accelerations that trigger the nonlinear behavior of vehicles) without jeopardizing human safety. The interest in this application is justified by the recent creation of racing challenges for full scale (Roborace 1 ), middle scale (Formula SAE 2 , see [6], [7]), and small scale (F1Tenth 3 ) vehicles. Autonomous racing capabilities for non-electric vehicles have been demonstrated e.g.…”

Section: Introductionmentioning

confidence: 99%

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Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars

Caporale

Settimi

Massa

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed.

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Section: Mapping and Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars

Caporale

Settimi

Massa

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…There have been growing demands for autonomous vehicles with excellent maneuvering capabilities both for commercial and military applications [1,2]. Significant amount of work is ongoing to realize the commercial operation of autonomous cars [3,4], and Wheeled Mobile Robots (WMRs) are finding increasing use in industrial and service applications [5]. Autonomous Surface Vehicles (ASV) have been utilized to improve port safety and for ecological as well as meteorological purposes [6], whereas Autonomous Underwater Vehicles (AUVs) are useful tools for underwater search and inspection [7].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

Bamgbose¹,

Li²

2019

Adv. sci. technol. eng. syst. j.

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For mission-critical and time-sensitive navigation of autonomous vehicles, controller design must exhibit excellent tracking performance with respect to the speed of convergence to reference command and steady-state accuracy. In this article, a novel design integration of the neural network with the traditional control system is proposed to adaptively obtain optimized controller parameters resulting in improved transient and steady-state performance of motion and position control of autonomous vehicles. Application of the proposed intelligent control scheme to mobile robot navigation was presented for an eightshaped trajectory by optimizing a Lyapunov-based nonlinear controller. Furthermore, a Linear Quadratic Regulator-based controller was optimized based on the proposed strategy to control the pitch and yaw angles of a 2-Degree-of -Freedom helicopter. The simulation results showed that the proposed scheme outperforms the traditional controllers in terms of the speed of convergence to the desired trajectory and overall error minimization.In order to execute maneuvers for unmanned Air Vehicles (UAVs), feedback linearization [24,25] and sliding mode control [26] have been proposed, but they have failed for certain models due to the nonlinear dynamics of the vehicle [27]. For helicopter control, backstepping control strategy [28] and Linear Quadratic Regulator (LQR)-based control [29] have been proposed.

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“…For example, an autonomous mobile robot which uses a complex navigation system, and requires high accuracy over many different environmental conditions, is the self-driving car. Such a system requires many expensive sensors and computational resources (for obvious safety reasons), such as: LIDAR, RADAR, GPS and video cameras, as well as complicated, compute intensive multi-sensor fusion, information fusion and visual processing algorithms [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Emulating the Functionality of Rodents’ Neurobiological Navigation and Spatial Cognition Cells in a Mobile Robot

Zeno

2015

IJC

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A unique roving robot navigational system is presented here, which is inspired by rats’ navigational and spatial awareness brain cells. Rodents, as well as all mammalians, are capable of exploring their surroundings when foraging or avoiding predators, and remembering their way home or to the closest known shelter through path integration. This is true for other creatures, but the neural cells involved in accomplishing these tasks have been most notably studied in rats, as they share certain similarities with a human’s brain. The robot built in this study, named ratbot, uses characteristics and interpreted functionalities of the specialized navigational and spatial cognition brain cells, which are primarily found in the hippocampus and entorhinal cortex. These cells are the: place cells, head direction cells, boundary cells, and grid cells, as well as memory used for the storage and access of salient distal cues. Similar to a rat, the ratbot uses path integration to navigate from one waypoint to another. This is accomplished through use of vectors and vector mathematics. Additionally, the ratbot uses a field programmable gate array (FPGA) to emulate grid cell inspired functionality for environment mapping and spatial cognition.

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Integration of Drive-by-Wire with Navigation Control for a Driverless Electric Race Car

Cited by 24 publications

References 20 publications

Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars

Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

Emulating the Functionality of Rodents’ Neurobiological Navigation and Spatial Cognition Cells in a Mobile Robot

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