A review for design and dynamics control of unmanned ground vehicle

Ni, Jun; Hu, Jibin; Chen, Xiang

doi:10.1177/0954407020912097

Cited by 58 publications

(33 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past few decades, the autonomous driving technology has rapidly developed. Fully autonomous vehicles are expected to be on the public roads in the next 30 years, which will not only reflect a country's advanced technology, but more importantly will help ease the traffic accidents and congestion [1][2]. Motion planning is a key component of autonomous vehicles.…”

Section: Motivationmentioning

confidence: 99%

Anti-rollover Motion Planning for Automated Heavy Trucks

Jin

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Anti-rollover is an important performance for automated heavy trucks, which has been seldomly considered in the motion planning. This paper proposes an anti-rollover motion planning based on model predictive control (MPC) for automated heavy trucks. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, a seven degrees of freedom rollover dynamics model is established, and an evaluation index that can accurately describe the rollover motion is derived for heavy trucks. Then, a model predictive control strategy is designed for motion planning that combines the rollover dynamics, the artificial potential field for obstacle avoidance, and the trajectory tracking. In addition, the optimal path is calculated that considers collision avoidance, anti-rollover and vehicle dynamic constraints. Furthermore, three typical scenarios are applied to validate the performance of the proposed motion planning algorithm. The obtained results demonstrate that the proposed anti-rollover motion planning can effectively avoid collisions and reduce the rollover risk simultaneously when confronting edge scenarios.

show abstract

Section: Motivationmentioning

confidence: 99%

Anti-rollover Motion Planning for Automated Heavy Trucks

Jin

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Autonomous driving has become a research and development hotspot in the automotive industry, since it has great potential in improving active safety, easing traffic congestion, and reducing energy consumption. Ni et al 1 comprehensively introduced various applications of autonomous vehicles in civilian and military areas and reviewed the development of dynamics control. The key technologies of autonomous vehicles consist of environment perception, decision making, motion planning, and trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Integrated longitudinal and lateral control for autonomous vehicles with active load transfer strategy at the handling limits

Liu

Yang

Leng

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In order to avoid collisions in emergency conditions, the autonomous vehicle may be necessitated to maneuvers up to their handling limits. In these scenarios, the vehicle must be able to react quickly although the longitudinal and lateral coupled and nonlinear dynamics are significantly enhanced. To improve the accuracy of trajectory tracking and vehicle stability at the handling limits, this paper presents a hierarchical motion control framework that prioritizing lateral response. For lateral motion control, a nested architecture that consists of lateral deviation control and yaw rate control is designed considering parameter uncertainties. In longitudinal motion controller, the constraints of longitudinal tire forces are calculated on the premise of meeting steering demand. On this basis, an active load transfer strategy is proposed to optimize the steering response, which increasing the lateral stiffness of the front wheels through load transfer generated by active braking. Meanwhile, a longitudinal speed control algorithm considering the longitudinal acceleration demand is developed based on conditional integral method, to cope with the contradiction between fast tracking and small overshoot. Finally, experimental results in different scenarios demonstrate that the autonomous vehicle with the proposed control scheme can track the desired trajectory stably and accurately at the handling limits.

show abstract

“…4 Vehicle motion planning is developed to generate the collision-free paths to avoid obstacles. 5 Potential field methods are utilized to provide the reference paths that navigate the unmanned ground vehicles to the destinations. 6 The potential values increase when the distances to the obstacles decrease.…”

Section: Introductionmentioning

confidence: 99%

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

Chen

Qin

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

show abstract

A review for design and dynamics control of unmanned ground vehicle

Cited by 58 publications

References 58 publications

Anti-rollover Motion Planning for Automated Heavy Trucks

Anti-rollover Motion Planning for Automated Heavy Trucks

Integrated longitudinal and lateral control for autonomous vehicles with active load transfer strategy at the handling limits

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

Contact Info

Product

Resources

About