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

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

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.

Year
2018
Attachments
Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots.pdf