Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners
College of Automation, Harbin Engineering University, Harbin 150001, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(1), 176; https://doi.org/10.3390/s19010176
Received: 13 November 2018
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Revised: 13 December 2018
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Accepted: 25 December 2018
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Published: 5 January 2019
(This article belongs to the Section Physical Sensors)
Robot navigation is a fundamental problem in robotics and various approaches have been developed to cope with this problem. Despite the great success of previous approaches, learning-based methods are receiving growing interest in the research community. They have shown great efficiency in solving navigation tasks and offer considerable promise to build intelligent navigation systems. This paper presents a goal-directed robot navigation system that integrates global planning based on goal-directed end-to-end learning and local planning based on reinforcement learning (RL). The proposed system aims to navigate the robot to desired goal positions while also being adaptive to changes in the environment. The global planner is trained to imitate an expert’s navigation between different positions by goal-directed end-to-end learning, where both the goal representations and local observations are incorporated to generate actions. However, it is trained in a supervised fashion and is weak in dealing with changes in the environment. To solve this problem, a local planner based on deep reinforcement learning (DRL) is designed. The local planner is first implemented in a simulator and then transferred to the real world. It works complementarily to deal with situations that have not been met during training the global planner and is able to generalize over different situations. The experimental results on a robot platform demonstrate the effectiveness of the proposed navigation system.
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Keywords:
robot navigation; global planning; end-to-end learning; local planning; reinforcement learning
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MDPI and ACS Style
Zhou, X.; Gao, Y.; Guan, L. Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners. Sensors 2019, 19, 176. https://doi.org/10.3390/s19010176
AMA Style
Zhou X, Gao Y, Guan L. Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners. Sensors. 2019; 19(1):176. https://doi.org/10.3390/s19010176
Chicago/Turabian StyleZhou, Xiaomao, Yanbin Gao, and Lianwu Guan. 2019. "Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners" Sensors 19, no. 1: 176. https://doi.org/10.3390/s19010176
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