A Path Planning and Navigation Control System Design for Driverless Electric Bus

Yu, Lingli; Kong, Decheng; Shao, Xuanya; Yan, Xiao‐Xin

doi:10.1109/access.2018.2868339

Cited by 45 publications

(32 citation statements)

References 40 publications

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“…Since the kinematic model ignores the lateral dynamic characteristics of the driverless bus [15], PPC-RAR achieves poor tracking performance in the driverless bus. Ignorance results in the oscillation of the driverless bus.…”

Section: Pure Pursuit Controller With a Front Axle Referencementioning

confidence: 99%

“…Especially, the road slope is obtained from the IMU. According to Newton's second law, the gravitational acceleration component in the forward direction of the driverless bus, which is shown in Equation 15, is used as the compensation of deceleration: sin( ) ff a g slope =  (15) where g is the gravitational acceleration ( g = 9.8 m/s 2 ), and slope is the road slope.…”

Section: Simulationsmentioning

confidence: 99%

“…A driverless bus system is built in our early work [15]. The global path-planning and local path-planning are available in this implementation.…”

Section: Real Bus Experimentsmentioning

confidence: 99%

“…However, they need different parameters to various conditions, which improve the complexity of path-tracking algorithms. Yu proposes an adaptive rolling optimization controller with preview window to track the desired path [15]. It is built on PID, and does not require a high-precision bus model.…”

Section: Introductionmentioning

confidence: 99%

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Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yan

Kuang

et al. 2019

Applied Sciences

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Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

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Section: Pure Pursuit Controller With a Front Axle Referencementioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

“…A driverless bus system is built in our early work [15]. The global path-planning and local path-planning are available in this implementation.…”

Section: Real Bus Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yan

Kuang

et al. 2019

Applied Sciences

Self Cite

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“…As the robot advances, the sub-window scrolls forward. This kind of repeated scrolling window local planning realizes a combination of optimization and feedback to complete the global planning [23].…”

Section: Rolling Window Path Planningmentioning

confidence: 99%

Indoor robot path planning assisted by wireless network

Wang

Nie

Zhu

2019

J Wireless Com Network

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Indoor robot global path planning needs to complete the motion between the starting point and the target point according to robot position command transmitted by the wireless network. Behavior dynamics and rolling windows in global path planning methods have limitations in their applications because the path may not be optimal, there could be a pseudo attractor or blind search in an environment with a large state space, there could be an environment where offline learning is not applicable to real-time changes, or there could be a need to set the probability of selecting the robot action. To solve these problems, we propose a behavior dynamics and rolling windows approach to a path planning which is based on online reinforcement learning. It applies Q learning to optimize the behavior dynamics model parameters to improve the performance, behavior dynamics guides the learning process of Q learning and improves learning efficiency, and each round of intensive learning action selection knowledge is gradually corrected as the Q table is updated. The learning process is optimized. The simulation results show that this method has achieved remarkable improvement in path planning. And, in the actual experiment, the robot obtains the target location information by wireless network, and plans an optimized and smooth global path online.

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Smart Garbage Monitoring System

Goel

2021

Smart and Sustainable Intelligent Systems

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A Path Planning and Navigation Control System Design for Driverless Electric Bus

Cited by 45 publications

References 40 publications

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Indoor robot path planning assisted by wireless network

Smart Garbage Monitoring System

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