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- Title
Deep Reinforcement Learning-Based Motion Control for Unmanned Vehicles from the Perspective of Multi-Sensor Data Fusion.
- Authors
Wei, Hongbo; Cui, Xuerong; Zhang, Yucheng; Chen, Haihua; Zhang, Jingyao
- Abstract
In this paper, the vehicle position points obtained by multi-sensor fusion are taken as the observed values, and Kalman filter is combined with the vehicle kinematics equation to further improve the vehicle trajectory. To realize this, mathematical principles of deep reinforcement learning are analyzed, and the theoretical basis of reinforcement learning is also analyzed. It is proved that the controller based on dynamic model is better than the controller based on kinematics in deviation control, and the performance of the controller based on deep reinforcement learning is also verified. The simulation data show that the proportion integration differentiation (PID) controller has a better tracking effect, but it does not have the constraint ability, which leads to radical acceleration change, resulting in unstable acceleration and deceleration control. Therefore, the deep reinforcement learning controller is selected as the longitudinal velocity tracking controller. The effectiveness of lateral and longitudinal motion decoupling strategy is verified by simulation experiments.
- Subjects
DEEP reinforcement learning; REINFORCEMENT learning; MULTISENSOR data fusion; AUTONOMOUS vehicles; REMOTELY piloted vehicles; KALMAN filtering
- Publication
Journal of Circuits, Systems & Computers, 2024, Vol 33, Issue 10, p1
- ISSN
0218-1266
- Publication type
Article
- DOI
10.1142/S0218126624501858