High speed path tracking application in harsh conditions: Predictive speed control to restrict the lateral deviation to some threshold

Braconnier, Jean-Baptiste; Lenain, Roland; Thuilot, Benoı̂t; Rousseau, Vincent

doi:10.1109/iros.2016.7759478

Cited by 1 publication

(3 citation statements)

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“…Thus a simple model can be used, based on curvature and speed limit [11], [12]. A model of the wheel ground interaction can also be considered [13], [14], [15]. This estimate is based on the angular velocity which predicts a situation of under-steering or over-steering.…”

Section: Introductionmentioning

confidence: 99%

“…The contribution proposed here is a neural network based online velocity controller. It is associated to an existing front steering Model-based Predictive Controller (MPC) for path tracking, that was previously implemented with a constant speed [14]. This task was previously addressed with a traditional approach [14], [13] which demonstrated the problem's complexity and the limits of this approach.…”

Section: Introductionmentioning

confidence: 99%

“…It is associated to an existing front steering Model-based Predictive Controller (MPC) for path tracking, that was previously implemented with a constant speed [14]. This task was previously addressed with a traditional approach [14], [13] which demonstrated the problem's complexity and the limits of this approach. The RL algorithms are used to solve this problem with more flexibility, as they are based on a data derived from a fully dynamic model with varying grip conditions.…”

Section: Introductionmentioning

confidence: 99%

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Online velocity fluctuation of off-road wheeled mobile robots: A reinforcement learning approach

Gauthier-Clerc

Hill

Laneurit

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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During the off-road path following of a wheeled mobile robot in presence of poor grip conditions, the longitudinal velocity should be limited in order to maintain safe navigation with limited tracking errors, while at the same time being high enough to minimize travel time. Thus, this paper presents a new approach of online speed fluctuation, capable of limiting the lateral error below a given threshold, while maximizing the longitudinal velocity. This is accomplished using a neural network trained with a reinforcement learning method. This speed modulation is done side-by-side with an existing model-based predictive steering control, using a state estimator and dynamic observers. Simulated and experimental results show a decrease in tracking error, while maintaining a consistent travel time when compared to a classical constant speed method and to a kinematic speed fluctuation method.

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