Motion Control of Non-Holonomic Constrained Mobile Robot Using Deep Reinforcement Learning

Author(s):  
Rui Gao ◽  
Xueshan Gao ◽  
Peng Liang ◽  
Feng Han ◽  
Bingqing Lan ◽  
...  
2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.


Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 48
Author(s):  
Mahmood Reza Azizi ◽  
Alireza Rastegarpanah ◽  
Rustam Stolkin

Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 15592-15602
Author(s):  
Xueshan Gao ◽  
Rui Gao ◽  
Peng Liang ◽  
Qingfang Zhang ◽  
Rui Deng ◽  
...  

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