Research on Path Planning Algorithm for Driverless Vehicles

In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve th...

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Vydané v:Mathematics (Basel) Ročník 10; číslo 15; s. 2555
Hlavní autori: Ma, Hao, Pei, Wenhui, Zhang, Qi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2022
Predmet:
Algorithms
artificial potential field method
Autonomous vehicles
Control systems design
driverless
Efficiency
Kinematics
Mathematics
model predictive control
Obstacle avoidance
Planning
Potential fields
Predictive control
R&D
rapidly exploring random tree algorithm
Research & development
Robots
Tires
Tracking control
Trajectory control
Trajectory planning
China
ISSN:2227-7390, 2227-7390
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Shrnutí:In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math10152555