@inproceedings{574fd31135e34ef5a9a3723930889c2a,
title = "DDPG-MPC: A Safe and Efficient Hybrid Path Planning Algorithm for Robots",
abstract = "Local path planning has always been an important problem in the field of robotics, aiming to make the control robot plan the collision-free optimal or sub-optimal path through local information. In this paper, a new safe hybrid local path planning method Deterministic Policy Gradient with Model Predictive Control (DDPG-MPC) is proposed to realize obstacle-free navigation for mobile robots. By combining reinforcement learning with model predictive control, the learnability and rapidity of reinforcement learning are preserved under the constraints of model predictive control. In order to realize more reliable and efficient local path planning, this method first uses DDPG method to train an obstacle-avoiding agent, which can generate the next action according to the current situation forward reasoning and hand it to the robot. Then, this paper uses the designed switcher to automatically switch between DDPG and MPC algorithms according to the constraints that the current robot needs to meet to realize local path planning. The experimental results show that the proposed hybrid local path planning algorithm DDPG-MPC is superior to pure DDPG in safety and MPC in efficiency.",
keywords = "Local path planning, MPC, Reinforcement learning, Trajectory tracking",
author = "Ao Ding and Licheng Sun and Minglei Han and Zhentao Guo and Tianhao Wang and Hongbin Ma",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024 ; Conference date: 20-09-2024 Through 22-09-2024",
year = "2024",
doi = "10.1109/CSIS-IAC63491.2024.10919372",
language = "English",
series = "2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "267--273",
booktitle = "2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024",
address = "United States",
}