Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Wang, Run Ying; Lin, Xu

doi:10.4028/www.scientific.net/amm.198-199.922

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In its learning process, the agent performs several interactions with its environment by making some actions that will cause a change of state in the environment and result in a positive or a negative reward (penalty) [ 35 ]. Over the years, RL has been the subject of many kinds of research in various applications such as chemical reaction [ 36 ], resource management [ 37 ], traffic-light management [ 38 ], autonomous driving [ 39 ], dam management [ 40 ], surgery [ 41 ] and robotics [ 42 ].…”

Section: Short Theory and Background Overviewmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

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Section: Short Theory and Background Overviewmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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show abstract

“…In short, it can be stated the agent learns the strategy to adopt through a series of trials-and-errors [9]. Over the years, RL has been the subject of many kinds of research in various applications such as chemical reaction [38], Resource Management [39], Traffic-light Management [40], Autonomous Driving [41], Dam Management [42], Surgery [43] and Robotics [44].…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

Kiangala

Wang

2021

Preprint

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The industrial manufacturing sector is currently undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer a static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. Operators are subject to frequent safety inductions to react in emergencies but very little is done for robots. Our research proposes a safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from emergency stops (ESTOPs) button. The factory emergency signal can be given by an ESTOP or a voice command sent directly to the factory central controller: an S7-1200 Siemens programmable logic controller (PLC) in this experiment. We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations.

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UAV-Assisted NOMA Network Power Allocation Under Offshore Multi-energy Complementary Power Generation System

Shao,

Lin,

et al. 2024

Lecture Notes in Electrical Engineering

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Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Cited by 3 publications

References 4 publications

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

UAV-Assisted NOMA Network Power Allocation Under Offshore Multi-energy Complementary Power Generation System

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