Efficient Path Tracking Control for Autonomous Driving of Tracked Emergency Rescue Robot under 6G Network
Author(s) -
Qing Gu,
Guoxing Bai,
Yu Meng,
Guodong Wang,
Jiazang Zhang,
Lei Zhou
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/5593033
Subject(s) - computer science , path (computing) , controller (irrigation) , matlab , rescue robot , model predictive control , real time computing , trajectory , feed forward , robot , enhanced data rates for gsm evolution , tracking (education) , mobile robot , simulation , control theory (sociology) , control (management) , artificial intelligence , control engineering , engineering , computer network , psychology , pedagogy , physics , astronomy , agronomy , biology , operating system
This paper proposes a path tracking control algorithm of tracked mobile robots based on Preview Linear Model Predictive Control (MPC), which is used to achieve autonomous driving in the unstructured environment under an emergency rescue scenario. It is the future trend to realize the communication and control of rescue equipment with 6G and edge cloud cooperation. In this framework, linear MPC (LMPC) is suitable for the path tracking control of rescue robots due to its advantages of less computing resources and good real-time performance. However, in such a scene, the driving environment is complex and the path curvature changes greatly. Since LMPC can only introduce linearized feedforward information, the tracking accuracy of the path with large curvature changes is low. To overcome this issue, combined with the idea of preview control, preview-linear MPC is designed in this paper. The controller is verified by MATLAB/Simulink simulation and prototype experiment. The results show that the proposed method can improve the tracking accuracy while ensuring real-time performance and has better tracking performance for the path with large curvature variation.
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