Mobile Robot Planning Based on RDL-Q Learning Algorithm

DOI

10.2991/meees-18.2018.74How to use a DOI?

Keywords

complex environment; mobile robot; path planning; Q learning algorithm; state trajectory; exploration strategy.

Abstract

Aiming at the problem of Q value update is slow for traditional Q learning algorithm in complex unknown environment, resulting in low learning efficiency and low real-time performance of mobile robot. A Reverse Double Linker Q (RDL-Q) learning algorithm is proposed. According to the state trajectory of the mobile robot, two state linkers are established to record the current sate-action pair and current state-reverse action pairs, from the value of the tail of a single chain, the current state, is traced back to the Q value at the end of a single linker head until the target is reached. Meanwhile, the Boltzmann search strategy combined with heuristic search strategy is used to guide the action selection strategy of the mobile robot learning process. The simulation results show that the algorithm can effectively speed up the convergence of learning algorithm and improve the learning efficiency in complex unknown environment and achieve the robot navigation task with the best path

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shengmin Wang
AU  - Wei Lin
PY  - 2018/05
DA  - 2018/05
TI  - Mobile Robot Planning Based on RDL-Q Learning Algorithm
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 420
EP  - 425
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.74
DO  - 10.2991/meees-18.2018.74
ID  - Wang2018/05
ER  -