@inproceedings{449a3b569a8449319d5f5dc46caf6884,
title = "Simultaneous Path Following and Obstacle Avoidance of Field Tracked Vehicles via Model Predictive Control with Deep Deterministic Policy Gradient",
abstract = "Unmanned ground vehicles (UGVs) will be widely adopted in agricultural applications. To accomplish autonomous cruising in farm, path following is an essential skill. However, in the process of field cruising, some obstacles such as wild animals or motorcycles are present. In this study, tracked vehicles are utilized with deep deterministic policy gradient (DDPG) compensating for model uncertainties and achieving collision avoidance simultaneously. Among all, the most important issue is to keep the UGV following the predetermined path in specific agricultural field environment and coping with the uncertainty of the surroundings. Path following and obstacle avoidance of field tracked vehicles are conducted by using model predictive control (MPC) with a controller (agent) trained by DDPG. Therefore, we proposed control algorithm fusion with MPC and model-free DDPG.",
keywords = "deep deterministic policy gradient, deep reinforcement learning, field tracked vehicle, model predictive control, obstacle avoidance, Path following",
author = "Sung, {Yu Cheng} and Sung, {Chun Ting} and Tseng, {Wen Chuan} and Chen, {Shean Jen}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; Optics and Photonics for Advanced Dimensional Metrology II 2022 ; Conference date: 09-05-2022 Through 20-05-2022",
year = "2022",
doi = "10.1117/12.2621549",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "{de Groot}, {Peter J.} and Leach, {Richard K.} and Pascal Picart",
booktitle = "Optics and Photonics for Advanced Dimensional Metrology II",
address = "United States",
}