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A Q-learning approach based on human reasoning for navigation in a dynamic environment

Published online by Cambridge University Press:  30 October 2018

Rupeng Yuan ,
Fuhai Zhang ,
Yu Wang ,
Yili Fu  and
Shuguo Wang
Rupeng Yuan
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. E-mails: yuanrupeng1991@163.com, yuwang_hit@163.com, meylfu_hit@163.com, wangxy_hit@163.com
Fuhai Zhang*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. E-mails: yuanrupeng1991@163.com, yuwang_hit@163.com, meylfu_hit@163.com, wangxy_hit@163.com
Yu Wang
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. E-mails: yuanrupeng1991@163.com, yuwang_hit@163.com, meylfu_hit@163.com, wangxy_hit@163.com
Yili Fu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. E-mails: yuanrupeng1991@163.com, yuwang_hit@163.com, meylfu_hit@163.com, wangxy_hit@163.com
Shuguo Wang
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. E-mails: yuanrupeng1991@163.com, yuwang_hit@163.com, meylfu_hit@163.com, wangxy_hit@163.com
*
*Corresponding author. E-mail: zfhhit@hit.edu.cn, meylfu_hit@163.com
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Summary

A Q-learning approach is often used for navigation in static environments where state space is easy to define. In this paper, a new Q-learning approach is proposed for navigation in dynamic environments by imitating human reasoning. As a model-free method, a Q-learning method does not require the environmental model in advance. The state space and the reward function in the proposed approach are defined according to human perception and evaluation, respectively. Specifically, approximate regions instead of accurate measurements are used to define states. Moreover, due to the limitation of robot dynamics, actions for each state are calculated by introducing a dynamic window that takes robot dynamics into account. The conducted tests show that the obstacle avoidance rate of the proposed approach can reach 90.5% after training, and the robot can always operate below the dynamics limitation.

Keywords

Autonomous navigationMobile robotDynamic environmentQ-learning
Type
Articles
Information
Robotica , Volume 37 , Issue 3 , March 2019 , pp. 445 - 468
Copyright
Copyright © Cambridge University Press 2018 

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