Ensuring path tracking stability of mobile robots in harsh conditions: An adaptive and predictive velocity control

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

doi:10.1109/icra.2014.6907633

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…• v max the maximum allowed speed. The measured yaw rate is included in the input data as it is an important indicator of vehicle stability [13]. The curvature is also a crucial variable for speed modulation as it defines the admissible yaw rate and speed (see deterministic alternative section II-B).…”

Section: Description Of the Selected Reinforcement Learning Approachmentioning

confidence: 99%

“…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%

“…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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Section: Description Of the Selected Reinforcement Learning Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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)

Self Cite

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show abstract

“…Zhenping Sun [26] used the road information and the state of vehicle to optimize the curvature and then calculated the steering output. Braconnier [27] improved the stability of mobile robots in harsh conditions by deriving the tracking law from the kinematic model. The sideslip angles required by model must be estimated online and also the velocity provided by an adaptive and predictive velocity controller.…”

Section: Control Theory Controllersmentioning

confidence: 99%

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Shan

Yang

Chen

et al. 2015

International Journal of Advanced Robotic Systems

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Simple and efficient geometric controllers, like PurePursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CFPursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C 1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6mcurvature, 9mcurvature and 12mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.

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

Braconnier¹,

Lenain²,

Thuilot

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Ensuring path tracking stability of mobile robots in harsh conditions: An adaptive and predictive velocity control

Cited by 5 publications

References 4 publications

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

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

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

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

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