Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

Ji Chang Kim; Dong Sung Pae; Myo Taeg Lim

doi:10.1007/s12555-019-9091-y

Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

Ji Chang Kim, Dong Sung Pae, Myo Taeg Lim

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	2850-2861
Number of pages	12
Journal	International Journal of Control, Automation and Systems
Volume	17
Issue number	11
DOIs	https://doi.org/10.1007/s12555-019-9091-y
Publication status	Published - 2019 Nov 1

Keywords

Comfort level
model predictive control
obstacle avoidance
path planning
vehicle dynamics

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications

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10.1007/s12555-019-9091-y

Cite this

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abstract = "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.",

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N2 - 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.

AB - 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.

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Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

Abstract

Keywords

ASJC Scopus subject areas

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