A Novel Method for Goal Recognition from 10 m Distance Using Deep Learning in CanSat
Miho Akiyama* and Takuya Saito**
*Graduate School of Electrical and Information Engineering, Shonan Institute of Technology
1-1-25 Tsujido-nishikaigan, Fujisawa, Kanagawa 251-8511, Japan
**Department of Information Science, Faculty of Engineering, Shonan Institute of Technology
1-1-25 Tsujido-nishikaigan, Fujisawa, Kanagawa 251-8511, Japan
In this study, we propose a method for CanSat to recognize and guide a goal using deep learning image classification even 10 m away from the goal, and describe the results of demonstrative evaluation to confirm the effectiveness of the method. We applied deep learning image classification to goal recognition in CanSat for the first time at ARLISS 2019, and succeeded in guiding it almost all the way to the goal in all three races, winning the first place as overall winner. However, the conventional method has a drawback in that the goal recognition rate drops significantly when the CanSat is more than 6–7 m away from the goal, making it difficult to guide the CanSat to the goal when it moves away from the goal because of various factors. To enable goal recognition from a distance of 10 m from the goal, we investigated the number of horizontal regions of interest divisions and the method of vertical shifts during image recognition, and clarified the effective number of divisions and recognition rate using experiments. Although object detection is commonly used to detect the position of an object from an image by deep learning, we confirmed that the proposed method has a higher recognition rate at long distances and a shorter computation time than SSD MobileNet V1. In addition, we participated in the CanSat contest ACTS 2020 to evaluate the effectiveness of the proposed method and achieved the zero-distance goal in all three competitions, demonstrating its effectiveness by winning first place in the comeback category.
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