acados—a modular open-source framework for fast embedded optimal control

Verschueren, Robin; Frison, Gianluca; Kouzoupis, Dimitris; Frey, Jonathan; Duijkeren, Niels van; Zanelli, Andrea; Novoselnik, Branimir; Albin, Thivaharan; Quirynen, Rien; Diehl, Moritz

doi:10.1007/s12532-021-00208-8

Cited by 166 publications

(81 citation statements)

References 68 publications

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“…A Volvo XC90 T6 AWD was used as the testing vehicle. The optimization of MPC problem is carried out through acados, an open source software package with a collection of solvers for fast embedded optimization intended for fast embedded applications [19]. Sequential Quadratic Programming (SQP) is used as the solver by acados.…”

Section: Simulation Resultsmentioning

confidence: 99%

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia¹,

Schimpe²,

Ferranti³

2021

Preprint

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Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, our ASS restricts the controller's authority to intervene for lateral correction of the human operator's commands, avoiding counter-intuitive driving experience for the human operator. Further, we design a visual feedback to enhance the operator's trust over the ASS. In addition, we propose an MPC's prediction horizon data based novel predictive display to mitigate the effects of large latency in the teleoperation system. We tested the performance of the proposed approach on a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.

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Section: Simulation Resultsmentioning

confidence: 99%

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia¹,

Schimpe²,

Ferranti³

2021

Preprint

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“…This approach is also tied to a positive definite stage cost function, linear dynamics and polytopic constraints, as well as a particular optimization approach for solving the MPC problem. By ensuring feasibility through a modified formulation of the predictive control problem, the presented approach allows for leveraging recent developments in the active field of fast real-time optimization techniques [18], [19] without relying on a specific optimization algorithm.…”

Section: B Related Workmentioning

confidence: 99%

Predictive control barrier functions: Enhanced safety mechanisms for learning-based control

Wabersich¹,

Zeilinger²

2021

Preprint

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While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assumptions and minor deviations can lead to failure of the filter putting the system at risk. This paper introduces an auxiliary softconstrained predictive control problem that is always feasible at each time step and asymptotically stabilizes the feasible set of the original safety filter, thereby providing a recovery mechanism in safety-critical situations. This is achieved by a simple constraint tightening in combination with a terminal control barrier function. By extending discrete-time control barrier function theory, we establish that the proposed auxiliary problem provides a 'predictive' control barrier function. The resulting algorithm is demonstrated using numerical examples.

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“…To ensure feasibility of the MPC problem (3), the track width constraint (10) and the terminal set constraint (16), are implemented as soft constraints. The problem is solved online using acados [25] with a real-time iteration SQP scheme, horizon length of N = 60, and sampling frequency of 80 Hz. The terminal set computation in (18) is solved offline using MOSEK [26], with n c = 21 equilibrium points spanning curvatures [−2.5, 2.5].…”

Section: A Problem Implementationmentioning

confidence: 99%

A predictive safety filter for learning-based racing control

Tearle¹,

Wabersich²,

Carron³

et al. 2021

Preprint

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The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development of formal safety verification techniques. In this paper, we design and implement a predictive safety filter that is able to maintain vehicle safety with respect to track boundaries when paired alongside any potentially unsafe control signal, such as those found in learning-based methods. A model predictive control (MPC) framework is used to create a minimally invasive algorithm that certifies whether a desired control input is safe and can be applied to the vehicle, or that provides an alternate input to keep the vehicle in bounds. To this end, we provide a principled procedure to compute a safe and invariant set for nonlinear dynamic bicycle models using efficient convex approximation techniques. To fully support an aggressive racing performance without conservative safety interventions, the safe set is extended in real-time through predictive control backup trajectories. Applications for assisted manual driving and deep imitation learning on a miniature remote-controlled vehicle demonstrate the safety filter's ability to ensure vehicle safety during aggressive maneuvers.

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acados—a modular open-source framework for fast embedded optimal control

Cited by 166 publications

References 68 publications

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Predictive control barrier functions: Enhanced safety mechanisms for learning-based control

A predictive safety filter for learning-based racing control

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