Facilitating Autonomous Vehicle Research and Development Using Robot Simulators on the Example of a KAMAZ NEO Truck

Abilkassov, Shyngyskhan; Nurlybayev, Anuar; Soltan, Sergey; Kim, Anton; Shpieva, Elizaveta; Yesmagambet, N.A.; Yessenbayev, Zhandos; Shintemirov, Almas

doi:10.1109/itsc45102.2020.9294716

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The software implementation of the solver was realized using the ACADO MPC toolkit, which also allows real-time embedded controller code generation [24]. The simulation experiments were run using the above-mentioned Webots robot simulator [22], that allows physical modeling of commercially available robots in realistic scenarios [25]. Specifically, a race track with stationary obstacles was modeled for running a Pioneer-3AT SSMR, controlled by the ACADO-generated controller as shown in Fig.…”

Section: A Control System Implementation and Simulation Setupmentioning

confidence: 99%

Spatial-Based Model Predictive Path Following Control for Skid Steering Mobile Robots

Dorbetkhany

Murbabulatov

Rubagotti

et al. 2022

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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This paper presents a model predictive path following control (MPPFC) framework for driving skid-steered mobile robots (SSMRs) in the presence of obstacles. A spatial kinematic model is used to develop a model along a predefined path while avoiding any incidental stationary obstacles.Extensive computation experiments executed on a physical robot simulator environment demonstrate that the proposed control approach effectively ensures robot convergence to a reference path with minimal deviations. The employed MPPFC parameters are presented for easy repeatability of the presented computation experiments and further utilization of the proposed control framework.

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Section: A Control System Implementation and Simulation Setupmentioning

confidence: 99%