scholarly journals Dynamic Window Approach with path-following for Unmanned Surface Vehicle based on Reinforcement Learning

2021 ◽  
Vol 24 (1) ◽  
pp. 61-69
Author(s):  
Jinyeong Heo ◽  
Jeesoo Ha ◽  
Junsik Lee ◽  
Jaekwan Ryu ◽  
Yongjin Kwon
Keyword(s):  
Reinforcement Learning ◽  
Path Following ◽  
Unmanned Surface Vehicle ◽  
Window Approach

Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning

2021 ◽  
Vol 110 ◽  
pp. 102590
Author(s):  
Shuwu Wang ◽  
Feng Ma ◽  
Xinping Yan ◽  
Peng Wu ◽  
Yuanchang Liu
Keyword(s):  
Reinforcement Learning ◽  
Unmanned Surface Vehicle ◽  
Vehicle Formations

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

2021 ◽  
pp. 095440702098357
Author(s):  
Ju Xie ◽  
Xing Xu ◽  
Feng Wang ◽  
Haobin Jiang
Keyword(s):  
Reinforcement Learning ◽  
Comprehensive Evaluation ◽  
Path Following ◽  
Intelligent Vehicles ◽  
Driver Model ◽  
Control Center ◽  
Training Performance ◽  
Learning Agents ◽  
System A ◽  
And Control

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

scholarly journals Adaptive Fast Non-Singular Terminal Sliding Mode Path Following Control for an Underactuated Unmanned Surface Vehicle with Uncertainties and Unknown Disturbances

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7454
Author(s):  
Yunsheng Fan ◽  
Bowen Liu ◽  
Guofeng Wang ◽  
Dongdong Mu
Keyword(s):  
Actuator Saturation ◽  
Sliding Mode ◽  
Path Following ◽  
Design Parameters ◽  
Real Time Control ◽  
Sideslip Angle ◽  
Terminal Sliding Mode ◽  
Unmanned Surface Vehicle ◽  
Control Approach ◽  
Unknown Disturbances

This paper focuses on an issue involving robust adaptive path following for the uncertain underactuated unmanned surface vehicle with time-varying large sideslips angle and actuator saturation. An improved line-of-sight guidance law based on a reduced-order extended state observer is proposed to address the large sideslip angle that occurs in practical navigation. Next, the finite-time disturbances observer is designed by considering the perturbations parameter of the model and the unknown disturbances of the external environment as the lumped disturbances. Then, an adaptive term is introduced into Fast Non-singular Terminal Sliding Mode Control to design the path following controllers. Finally, considering the saturation of actuator, an auxiliary dynamic system is introduced. By selecting the appropriate design parameters, all the signals of the whole path following a closed-loop system can be ultimately bounded. Real-time control of path following can be achieved by transferring data from shipborne sensors such as GPS, combined inertial guidance and anemoclinograph to the Fast Non-singular Terminal Sliding Mode controller. Two examples as comparisons were carried out to demonstrate the validity of the proposed control approach.

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