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JACIII Vol.25 No.1 pp. 110-120
doi: 10.20965/jaciii.2021.p0110
(2021)

Paper:

Ameliorated Frenet Trajectory Optimization Method Based on Artificial Emotion and Equilibrium Optimizer

Xiangdong Wu, Kaoru Hirota, Bijun Tang, Yaping Dai, and Zhiyang Jia

School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Corresponding author

Received:
October 25, 2020
Accepted:
November 17, 2020
Published:
January 20, 2021
Keywords:
trajectory planning, Frenet trajectory optimization, artificial emotion, equilibrium optimizer
Abstract
Ameliorated Frenet Trajectory Optimization Method Based on Artificial Emotion and Equilibrium Optimizer

Trajectory of UGV for AFTO method

An ameliorated Frenet trajectory optimization (AFTO) method based on artificial emotion (AE) and an equilibrium optimizer (EO) is proposed for the local trajectory planning of an unmanned ground vehicle (UGV). An artificial emotional potential field (AEPF) model is established to simulate AE. To realize a humanoid driving mode with emotional intelligence, AE is introduced into the Frenet trajectory optimization (FTO) method to determine the optimal trajectory. Based on the optimal discrete goal state of the FTO method, a first-sampling-then-optimization (FSTO) framework combining the FTO method with the EO is designed to obtain the optimal trajectory in a continuous goal state space. With different AEPF levels corresponding to different types of obstacles, simulation results show that the AEPF effectively adjusts the trajectory into different levels of safe distance between the UGV and obstacles corresponding to the humanoid driving mode. From the results of 30 independent experiments based on the AEPF, the FSTO framework in the AFTO method is effective for optimizing the trajectory of the FTO method at a lower cost. Moreover, the effectiveness of the proposed method for different types of roads is verified on a straight road and a curved road with obstacles in simulation. The improvement based on emotional intelligence and trajectory optimization in the AFTO method provides a humanoid driving mode for the UGV in the continuous goal state space.

Cite this article as:
Xiangdong Wu, Kaoru Hirota, Bijun Tang, Yaping Dai, and Zhiyang Jia, “Ameliorated Frenet Trajectory Optimization Method Based on Artificial Emotion and Equilibrium Optimizer,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 110-120, 2021.
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Last updated on Jul. 31, 2021