Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments

Robotics

Yusuke Takayama¹  Photchara Ratsamee¹  Tomohiro Mashita¹ 

[1] Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita 565-0871, Japan;

关键词: reduced simulation;    collision detection;    micro aerial vehicles;   

DOI  :  10.3390/robotics10040131

来源: DOAJ

【 摘 要 】

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario.

【 授权许可】

Unknown