Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

Ayoub, Raji,; Liniger, Alexander; Giove, Andrea; Toschi, A.; Musiu, Nicola; Morra, Daniele; Verucchi, Micaela; Caporale, Danilo; Bertogna, Marko

doi:10.1109/itsc55140.2022.9922239

Cited by 23 publications

(22 citation statements)

References 21 publications

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“…For multi-agent racing control, there are works [21] [22] on using rule-based strategy selection and using high-level path planner/low-level control to execute strategies like overtaking, blocking, collision avoidance, etc. However, these works rely on a lot of parameters and it always difficult to find an optimal set of parameters to work on all track maps and environments.…”

Section: Related Workmentioning

confidence: 99%

Delay-aware Robust Control for Safe Autonomous Driving

Kalaria

Dolan

2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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Delays endanger safety of autonomous systems operating in a rapidly changing environment, such as nondeterministic surrounding traffic participants in autonomous driving and high-speed racing. Unfortunately, delays are typically not considered during the conventional controller design or learningenabled controller training phases prior to deployment in the physical world. In this paper, the computation delay from nonlinear optimization for motion planning and control, as well as other unavoidable delays caused by actuators, are addressed systematically and unifiedly. To deal with all these delays, in our framework: 1) we propose a new filtering approach with no prior knowledge of dynamics and disturbance distribution to adaptively and safely estimate the time-variant computation delay; 2) we model actuation dynamics for steering delay; 3) all the constrained optimization is realized in a robust tube model predictive controller. For the application merits, we demonstrate that our approach is suitable for both autonomous driving and autonomous racing. Our approach is a novel design for a standalone delay compensation controller. In addition, in the case that a learning-enabled controller assuming no delay works as a primary controller, our approach serves as the primary controller's safety guard. The video demonstration is available online 1 .

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Section: Related Workmentioning

confidence: 99%

Delay-aware Robust Control for Safe Autonomous Driving

Kalaria

Dolan

2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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“…The individual trajectories are checked for feasibility, and the optimal valid one is selected according to a cost function. Raji et al [1] use these jerk-optimal curves for racing on oval race tracks. In [2], the jerk-optimal edges are used to generate the initial edges connecting the vehicle state to a coarse spatio-temporal graph computed offline.…”

Section: A Related Workmentioning

confidence: 99%

“…Applied to our case, quintic polynomials can be used for curve generation in the lateral direction and quartic polynomials in the longitudinal direction. The resulting jerk-optimal curves in curvilinear coordinates reduce abrupt acceleration changes, which has a beneficial effect on the subsequent tracking control as well as vehicle stability and are used in the context of the racing scenario in [1] and [2]. On the other hand, these trajectories exhibit only a simple structure, which generally cannot represent the racing line geometry for a complex race track.…”

Section: Trajectory Generationmentioning

confidence: 99%

“…After a local planning cycle, the ego and the opposing vehicles are moved along the planned trajectory and prediction by a fixed time ∆t sim , ensuring a deterministic simulation. 1 All parameters used for the experiments are listed in Table I.…”

Section: A Computational Detailsmentioning

confidence: 99%

“…The individual trajectories are checked for feasibility, and the optimal one is selected based on a predefined cost function. While sampling-based approaches have been successfully applied to oval race tracks [1]- [3], following the racing line on a complex circuit in the presence of opposing vehicles presents greater challenges. Furthermore, the existing approaches are limited to two-dimensional (2D) race tracks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smooth Trajectory Planning at the Handling Limits for Oval Racing

et al. 2022

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In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle.

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ForzaETH Race Stack—Scaled Autonomous Head‐to‐Head Racing on Fully Commercial Off‐the‐Shelf Hardware

Baumann,

Ghignone,

Kühne

et al. 2024

Journal of Field Robotics

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Autonomous racing in robotics combines high‐speed dynamics with the necessity for reliability and real‐time decision‐making. While such racing pushes software and hardware to their limits, many existing full‐system solutions necessitate complex, custom hardware and software, and usually focus on Time‐TrIals rather than full unrestricted Head‐to‐head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well‐resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head‐to‐Head autonomous racing competition, which simplifies replication by using commercial off‐the‐shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout, which is exemplified by the various modularly implemented state estimation and control systems. Capable of handling both Time‐Trials and Head‐to‐Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times. Furthermore, the stack demonstrated its reliability and performance at unprecedented scales, up to over on tracks up to 150 m in length.

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Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

Cited by 23 publications

References 21 publications

Delay-aware Robust Control for Safe Autonomous Driving

Delay-aware Robust Control for Safe Autonomous Driving

Smooth Trajectory Planning at the Handling Limits for Oval Racing

ForzaETH Race Stack—Scaled Autonomous Head‐to‐Head Racing on Fully Commercial Off‐the‐Shelf Hardware

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