NMPC for Racing Using a Singularity-Free Path-Parametric Model with Obstacle Avoidance

Kloeser, Daniel; Schoels, Tobias; Sartor, Tommaso; Zanelli, Andrea; Prison, Gianluca; Diehl, Moritz

doi:10.1016/j.ifacol.2020.12.1376

Cited by 39 publications

(9 citation statements)

References 14 publications

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“…A third strategy is to combine the coordinate transformation with the usual temporal discretization of MPC [15]- [19]. In contrast to the spatial discretization, the vehicle dynamics can be incorporated in the trajectory planning problem by expressing the vehicle model based on the track coordinate system.…”

Section: B Related Workmentioning

confidence: 99%

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

Scheffe

Henneken

Kloock

et al. 2023

IEEE Trans. Intell. Veh.

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Section: B Related Workmentioning

confidence: 99%

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

Scheffe

Henneken

Kloock

et al. 2023

IEEE Trans. Intell. Veh.

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“…In Verschueren et al [49], the same authors extended their work by including a full nonlinear dynamic bicycle model and using the same time‐optimal control approach with spatial reformulation. In a similar approach, Kloeser et al [50] addressed autonomous racing for a 1:43 scale race car using a singularity‐free path parametric model for NMPC predictions. Contrary to Verschueren et al [48], they used partial spatial reformulation of the model to exclude singularities.…”

Section: Vehicle Controlmentioning

confidence: 99%

“…The super-twisting algorithm ensures robust stability and reduces the chattering phenomenon; let us FIGURE 7 Sliding mode control (SMC) principle consider a system of the form: . x = 𝑓 (t, x) + g(t, x)u(t) (50) with 𝑓 and g being continuous functions and x and u the state vector and the control input, respectively. Let us now define a sliding variable s with a derivative expressed as follows:…”

Section: Sliding Mode Controllermentioning

confidence: 99%

Lateral control for autonomous wheeled vehicles: A technical review

Kebbati

Ait-Oufroukh

Ichalal

et al. 2022

Asian Journal of Control

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Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments.

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“…In this work, we therefore propose to use MPC as an experience source for RL in the case of sparse rewards. MPC has been very popular lately in robotics and industry [12]- [17] as it is able to handle constraints on both states and control signals, can handle multiple-input multiple-output systems as well as nonlinear systems, and the cost function can be constructed in a straightforward way by minimizing the deviation between the reference states and the current states. The aim of our work is therefore to show that MPC can be used to provide demonstrations for an RL agent in sparse reward settings to solve a specific task.…”

Section: Demonstrations Actionmentioning

confidence: 99%

Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

Dawood¹,

Dengler²,

Jorge³

et al. 2022

Preprint

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Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious finetuning to ensure that agents are able to learn the desired behaviour. Using a sparse reward conveniently mitigates these challenges. However, the sparse reward represents a challenge on its own, often resulting in unsuccessful training of the agent. In this paper, we therefore address the sparse reward problem in RL. Our goal is to find an effective alternative to reward shaping, without using costly human demonstrations, that would also be applicable to a wide range of domains. Hence, we propose to use model predictive control (MPC) as an experience source for training RL agents in sparse reward environments. Without the need for reward shaping, we successfully apply our approach in the field of mobile robot navigation both in simulation and real-world experiments with a Kuboki Turtlebot 2. We furthermore demonstrate great improvement over pure RL algorithms in terms of success rate as well as number of collisions and timeouts. Our experiments show that MPC as an experience source improves the agent's learning process for a given task in the case of sparse rewards.

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NMPC for Racing Using a Singularity-Free Path-Parametric Model with Obstacle Avoidance

Cited by 39 publications

References 14 publications

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

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

Lateral control for autonomous wheeled vehicles: A technical review

Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

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