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- 저자 : Dianbo Ren (검색결과 2 건)
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Weihua Li,Yipeng Wang,Junlong Guo,Dianbo Ren,Jianfeng Wang,Shengkai Zhu,Jianping Xiao,Shijuan Chen 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.5
This study proposes an improved artificial potential field (APF) by considering the cooperative control of local obstacle avoidance and path tracking for automated vehicles. We established the path gravitational potential field (GPF) based on the scheduled path (SP), including the lateral and longitudinal GPFs, to enable the automated vehicle to quickly return to the SP and track after obstacle avoidance, while maintaining control of speed for the entire process. To address the local optimal solution problem of the classical APF, we proposed a sub-target-point selection strategy based on the information of obstacles and SP and established the GPF of the sub-target points. Thus, the automated vehicle can avoid obstacles and quickly return to the SP. Furthermore, the relative velocity of the automated vehicle and the obstacle was used to establish the velocity repulsion potential field (RPF), which improved the adaptability of the APF to dynamic obstacles. The simulation results indicate that the improved APF is capable of cooperative control of path tracking and local obstacle avoidance. Code is available at https://github.com/xiaowang617/Improve-APF.
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Driver Preview Model with Dual Far-near Points for Autonomous Vehicles
Rongrong Xu,Zezheng Huang,Weihua Li,Jianfeng Wang,Dianbo Ren,Xuewen Geng 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.11
This paper proposes a driver preview-based path-following controller to control both the lateral and longitudinal movements of a vehicle. First, a lateral tracking controller with two preview points is established by considering the displacement and heading errors of the two preview points: the far point, determined by the vehicle speed and fixed preview time, and the near point located at the center of the front axle. Depending on different parameters of the road input, the steering wheel angle is calculated, and different weights are assigned to the steering wheel angles corresponding to different road inputs. Next, a longitudinal tracking controller is established, which controls the vehicle velocity based on the road information of the far point. The control objects are the brake and accelerator pedals. Subsequently, the coupling of the lateral and longitudinal motion of the vehicle is analyzed, and an integrated longitudinal and lateral tracking controller is established. To verify the performance of the controller, the controller and vehicle model are established in Simulink and CarSim, respectively, to enable joint simulation. It is observed that the coupling is solved, and the near-point control makes the tracking error converge to zero and enhances the control effect. It demonstrates high adaptability and control accuracy of the proposed controller.
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