Multi-robot path planning based on a deep reinforcement learning DQN algorithm

The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high r...

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Vydané v:	CAAI Transactions on Intelligence Technology Ročník 5; číslo 3; s. 177 - 183
Hlavní autori:	Yang, Yang, Juntao, Li, Lingling, Peng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Beijing The Institution of Engineering and Technology 01.09.2020 John Wiley & Sons, Inc Wiley
Predmet:	algorithmic process Algorithms Automation classic deep reinforcement learning algorithm Decision making Deep learning deep Q‐network algorithm handling robot improved DQN algorithm converges learning (artificial intelligence) Machine learning mobile robots multi-robot systems multirobot path planning Neural networks Path planning path-planning problems Q‐learning algorithm Research Article robot path-planning problem Robots scheduling system algorithm unmanned warehouse dispatching system volume-based technology warehouse picking operation Warehouses warehouse picking operation scheduling system algorithm multirobot path planning multi-robot systems unmanned warehouse dispatching system path-planning problems algorithmic process mobile robots path planning robot path-planning problem volume-based technology improved DQN algorithm converges learning (artificial intelligence) Q-learning algorithm classic deep reinforcement learning algorithm deep Q-network algorithm handling robot
ISSN:	2468-2322, 2468-6557, 2468-2322
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Shrnutí:	The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high requirements. This study uses a deep Q-network (DQN) algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, an empirical playback mechanism, and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem: slow convergence and excessive randomness. Preceding the start of the algorithmic process, prior knowledge and prior rules are used to improve the DQN algorithm. Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems. This improves the efficiency of multi-robot path planning.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2468-2322 2468-6557 2468-2322
DOI:	10.1049/trit.2020.0024