ArticleSensors (Basel, Switzerland)2022
Path-Tracking Control Strategy of Unmanned Vehicle Based on DDPG Algorithm.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Research on robot path tracking method based on IDDPG-MPC.PloS one · 2026Article
- Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction.Sensors (Basel, Switzerland) · 2024Article
- Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC.Sensors (Basel, Switzerland) · 2023Article
- Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks.Sensors (Basel, Switzerland) · 2023Article
- A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.