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

Autor: Peng Lingling, Yang Yang, Li Juntao
Rok vydání: 2020
Předmět:
0209 industrial biotechnology
multi-robot systems
Computer Networks and Communications
Computer science
02 engineering and technology
scheduling system algorithm
improved dqn algorithm converges
deep q-network algorithm
020901 industrial engineering & automation
mobile robots
Artificial Intelligence
0202 electrical engineering
electronic engineering
information engineering
Reinforcement learning
q-learning algorithm
Motion planning
classic deep reinforcement learning algorithm
path planning
algorithmic process
Randomness
lcsh:Computer software
multirobot path planning
volume-based technology
robot path-planning problem
Artificial neural network
path-planning problems
unmanned warehouse dispatching system
Volume (computing)
Process (computing)
lcsh:P98-98.5
Mobile robot
warehouse picking operation
Human-Computer Interaction
lcsh:QA76.75-76.765
Robot
learning (artificial intelligence)
020201 artificial intelligence & image processing
handling robot
Computer Vision and Pattern Recognition
lcsh:Computational linguistics. Natural language processing
Algorithm
Information Systems
Zdroj: CAAI Transactions on Intelligence Technology (2020)
ISSN: 2468-2322
Popis: 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.
Databáze: OpenAIRE
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