深層学習に基づく非構造環境下での履帯式知能輸送車両の安定障害物回避制御戦略に関する研究
Research on Stable Obstacle Avoidance Control Strategy for Tracked Intelligent Transportation Vehicles in Non-structural Environment Based on Deep Learning
Attention-LSTM深層学習モデルを用いて、非構造環境で履帯式無人車両が安定して障害物を回避する制御戦略を提案し、実験で有効性を検証した。
著者: Yitian Wang, Jun Lin, Liu Zhang, Tianhao Wang, Hao Xu, Guanyu Zhang, Yang Liu
分類: cs.RO
原文アブストラクト
Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on Attention-long short-term memory (Attention-LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, we improved the autonomous obstacle avoidance control strategy combined with the stability analysis of special vehicles. Third, we constructed a deep learning obstacle avoidance control through experiments, and the average relative error of this system was 15%. Finally, the stability and accuracy of this control strategy were verified numerically and experimentally. The method proposed in this study can ensure that the unmanned vehicle can successfully avoid the obstacles while driving smoothly.
