Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

Image processing and control technologies have been widely studied and autonomous vehicles have become an active research area. For autonomous driving, it is essential to generate a safe obstacle avoidance path considering the surrounding environment. This paper devised an algorithm based on a real-...

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Bibliographic Details
Published inInternational journal of control, automation, and systems Vol. 17; no. 11; pp. 2850 - 2861
Main Authors Kim, Ji-Chang, Pae, Dong-Sung, Lim, Myo-Taeg
Format Journal Article
LanguageEnglish
Published Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.11.2019
Springer Nature B.V
제어·로봇·시스템학회
Subjects
Algorithms
Computer simulation
Control
Control algorithms
Control theory
Engineering
High speed
Image processing
Mechatronics
Obstacle avoidance
Path planning
Predictive control
Product design
Robotics
제어계측공학
model predictive control
obstacle avoidance
vehicle dynamics
path planning
Comfort level
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Summary:Image processing and control technologies have been widely studied and autonomous vehicles have become an active research area. For autonomous driving, it is essential to generate a safe obstacle avoidance path considering the surrounding environment. This paper devised an algorithm based on a real-time output constrained model predictive control for obstacle avoidance path planning in high speed driving situations. The proposed algorithm was compared with the normal model predictive control algorithm by simulation, including operation times to verify robustness for high speed driving situations. We used the ISO 2631-1 comfort level standard to quantify driver comfort fo r both cases.
Bibliography:http://link.springer.com/article/10.1007/s12555-019-9091-y
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-019-9091-y