Trajectory Generation From Paths for Autonomous Ground Vehicles

2020 ◽  
Author(s):  
Letian Lin ◽  
J. Jim Zhu
Keyword(s):  
Phase Plane ◽  
Longitudinal Velocity ◽  
Initial Boundary ◽  
Limit Function ◽  
Ground Vehicles ◽  
Reverse Time ◽  
Dynamic Constraints ◽  
Autonomous Ground Vehicles ◽  
Practical Concern ◽  
Initial Boundary Condition

Abstract Path-to-trajectory conversion problem for car-like autonomous ground vehicles has been studied in various ways. It is challenging to generate a trajectory which is dynamically feasible for the vehicle and comfortable for the passengers. An important practical concern of low computational costs makes the problem even more difficult. In this work, a path-to-trajectory converter is developed using a novel receding-horizon type suboptimal algorithm. By transforming the dynamic constraints to a longitudinal velocity limit function in the velocity-acceleration phase plane, a time-sub-optimal trajectory satisfying the dynamic constraints and the initial boundary condition is generated by computing the maximum constant acceleration in the down-range horizon. The portion of the trajectory approaching the end of the path is generated in reverse time. As illustrated by some simulation results and validation on a full-scale Kia Soul EV, the proposed path-to-trajectory conversion algorithm is able to account for dynamic constraints of the vehicle and guarantees passenger comfort.

Observer-based state-feedback robust control for path following of autonomous ground vehicles

2019 ◽  
Vol 234 (2) ◽  
pp. 222-239 ◽  
Author(s):  
Pengpeng Feng ◽  
Jianwu Zhang ◽  
Weimiao Yang
Keyword(s):  
State Feedback ◽  
Longitudinal Velocity ◽  
Path Following ◽  
Linear Matrix ◽  
Ground Vehicles ◽  
Parameter Uncertainties ◽  
Lateral Velocity ◽  
External Disturbances ◽  
Static State ◽  
Autonomous Ground Vehicles

In this article, a robust [Formula: see text] observer-based static state-feedback controller is designed for the path following of autonomous ground vehicles. The Takagi–Sugeno fuzzy modeling technique is used for modeling of vehicle dynamics with varying longitudinal velocity first. Then considering the high cost of direct lateral velocity measurement, an observer is designed to estimate the value of lateral velocity. Meanwhile, a robust controller is proposed to deal with the parameter uncertainties and external disturbances simultaneously, including the variation of the tire-cornering stiffness of both front and rear tires. Afterward, the condition of designing such an observer-based controller is transformed into the feasible problem of linear matrix inequalities. Numerical simulations using a high-fidelity and full vehicle model are performed based on a Carsim–Simulink joint platform. Simulation results under different conditions and comparison with other controller show that the proposed controller is effective irrespective of the variation in the road condition, the change in the vehicle longitudinal velocity and the external disturbances.

Fuzzy Control for Autonomous Ground Vehicles

2000 ◽  
Author(s):  
Jeffrey S. Wit ◽  
Carl D. Crane ◽  
Armstrong III ◽  
II David G.
Keyword(s):  
Fuzzy Control ◽  
Ground Vehicles ◽  
Autonomous Ground Vehicles

scholarly journals Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hajira Saleem ◽  
Faisal Riaz ◽  
Leonardo Mostarda ◽  
Muaz A. Niazi ◽  
Ammar Rafiq ◽  
...  
Keyword(s):  
Ground Vehicles ◽  
Steering Angle ◽  
Autonomous Ground Vehicles ◽  
Prediction Techniques

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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