Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network
Wenkai Chang, Peng Li, Caiyun Yang, Tao Lu, Yinghao Cai, Shuo Wang
Abstract
In this paper, a self-modeling method based on a causal network is proposed for the tracking control of the Crawler Fire Fighting Robot (CFFR). The method mainly consists of two parts, one is a motion model, based on data driving, learning to establish the correspondence between control signal sequence and vehicle motion, estimating the motion state of the next moment from historical data, eliminating complex CFFR modeling. The other is the tracking network. Based on the simulation data of the motion model, the relationship between the target trajectory and the current control command is learned, which simplifies the design and cumbersome tuning of the complex controller. The effectiveness of the proposed method is verified in both simulated and real-world environments. Qualitative and quantitative experimental results verify the accuracy of the tracking.
BibTeX
@inproceedings{iros2019_selfmodelingtrac,
title = {Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network},
author = {Wenkai Chang and Peng Li and Caiyun Yang and Tao Lu and Yinghao Cai and Shuo Wang},
booktitle = {IROS 2019},
year = {2019}
}