Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots

저자
Gilhyun Ryou, Youngwoo Sim, Seong Ho Yeon, Sangok Seok
인용
IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21 - 25 May 2018
초록

In this paper, we propose a new methodology to embed deep learning-based algorithms in both visual recognition and motion planning for general mobile robotic platforms. A framework for an asynchronous deep classification network is introduced to integrate heavy deep classification networks into a mobile robot with no loss of system bandwidth. Moreover, a gaming reinforcement learning-based motion planner, a novel and convenient embodiment of reinforcement learning, is introduced for simple implementation and high applicability. The proposed approaches are implemented and evaluated on a developed robot, TT2-bot. The evaluation was based on a mission devised for a qualitative evaluation of the general purposes and performances of a mobile robotic platform. The robot was required to recognize targets with a deep classifier and plan the path effectively using a deep motion planner. As a result, the robot verified that the proposed approaches successfully integrate deep learning technologies on the stand-alone mobile robot. The embedded neural networks for recognition and path planning were critical components for the robot.

발행년도
2018
파일 다운로드
Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots.pdf (1.58MB)