Real-time optimal control of an autonomous RC car with minimum-time maneuvers and a novel kineto-dynamical model

Pagot, Edoardo; Piccinini, Mattia; Biral, Francesco

doi:10.1109/iros45743.2020.9340640

“…This racing line is the fastest trajectory for the vehicle that needs to be followed when there are no opponents around the car. Under specific optimization objectives like minimum curvature (Braghin et al, 2008;Heilmeier et al, 2019), minimum time (Christ et al, 2019;Pagot et al, 2020) and minimum energy (Herrmann et al, 2019) there are a variety of solutions to this problem. Local planning aims to achieve a high planning horizon for recursive feasibility while avoiding opponent vehicles with evasive maneuvers at high speeds.…”

Section: Softwarementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

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For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km h −1 and 28 m s −2 . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.

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“…Due to the low computation time, a long horizon can be used, resulting in close to optimal trajectories. In contrast to other publications, the goal of optimization of [6] is the minimization of lap time instead of maximization of track progress.…”

Section: A Related Workmentioning

confidence: 99%

Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing

Alrifaee¹,

Scheffe²,

Kloock³

et al. 2022

Preprint

0

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<div>We present a real-time-capable Model Predictive Controller (MPC) based on a single-track vehicle model and Pacejka’s magic tire formula for autonomous racing applications. After formulating the general non-convex trajectory optimization problem, the model is linearized around estimated operating points and the constraints are convexified using the Sequen- tial Convex Programming (SCP) method. We use two different methods to convexify the non-convex track constraints, namely Sequential Linearization (SL) and Sequential Convex Restriction (SCR). SL, a method of relaxing the constraints, was introduced in our previous paper. SCR, a method of restricting the con- straints, is introduced in this paper. We show the application of SCR to autonomous racing and prove that it does not interfere with recursive feasibility. We compare the predicted trajectory quality for the nonlinear single-track model to the linear double integrator model from our previous paper. The MPC performance is evaluated on a scaled version of the Hockenheimring racing track. We show that an MPC with SCR yields faster lap times than an MPC with SL – for race starts as well as flying laps – while still being real-time capable. A video showing the results is available at https://youtu.be/21iETsolCNQ.<br></div>

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“…Many papers showed real-world implementations of their formulated problems, e.g., [3], [6]. The authors of [2] showed the implementation of stochastic optimal control on GPUs for a rally car on loose ground thus operating within friction limits of the tires.…”

Section: A Related Workmentioning

confidence: 99%

“…Recently, [6] proposed an approach using a kineto-dynamic model, determining parameters by experiments. Due to the low computation time, a long horizon can be used, resulting in close to optimal trajectories.…”

Section: A Related Workmentioning

confidence: 99%

Sequential Convex Programming Methods for Real-time Trajectory Optimization in Autonomous Vehicle Racing

Alrifaee¹,

Scheffe²,

Kloock³

et al. 2021

Preprint

2

0

View full text Add to dashboard Cite

<div>We present a real-time-capable Model Predictive Controller (MPC) based on a single-track vehicle model and Pacejka’s magic tire formula for autonomous racing applications. After formulating the general non-convex trajectory optimization problem, the model is linearized around estimated operating points and the constraints are convexified using the Sequen- tial Convex Programming (SCP) method. We use two different methods to convexify the non-convex track constraints, namely Sequential Linearization (SL) and Sequential Convex Restriction (SCR). SL, a method of relaxing the constraints, was introduced in our previous paper. SCR, a method of restricting the con- straints, is introduced in this paper. We show the application of SCR to autonomous racing and prove that it does not interfere with recursive feasibility. We compare the predicted trajectory quality for the nonlinear single-track model to the linear double integrator model from our previous paper. The MPC performance is evaluated on a scaled version of the Hockenheimring racing track. We show that an MPC with SCR yields faster lap times than an MPC with SL – for race starts as well as flying laps – while still being real-time capable. A video showing the results is available at https://youtu.be/21iETsolCNQ.<br></div>

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Real-time optimal control of an autonomous RC car with minimum-time maneuvers and a novel kineto-dynamical model

Cited by 21 publications

References 23 publications

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing

Sequential Convex Programming Methods for Real-time Trajectory Optimization in Autonomous Vehicle Racing

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