Hybrid Obstacle Avoidance and Simulation of Carrier-Based Aircraft on the Deck of an Aircraft Carrier

EasyChair Preprint 5372

Abstract

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environment. Existing methods have the problem of unstable effects when dealing with dynamic obstacles, and many methods have discrete decision spaces. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. We employ Unity 3D to perform simulation experiments in highly uncertain environment. The results show that our method has made great improvement on accuracy by 7%-30% compared with the other methods, and on the length of the path and turning angle by reducing 100 units and 400-450 degrees compared with DQN (Deep Q Network), respectively.

Keyphrases: MPC, 人工势场, 路径规划, 避障

@booklet{EasyChair:5372,
  author    = {Xue Junxiao and Kong Xiangyan and Dong Bowei and Guo Yibo and Xu Mingliang},
  title     = {Hybrid Obstacle Avoidance and Simulation of Carrier-Based Aircraft on the Deck of an Aircraft Carrier},
  howpublished = {EasyChair Preprint 5372},
  year      = {EasyChair, 2021}}