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

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 huma...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 22; číslo 3; s. 941
Hlavní autoři:	Kiangala, Kahiomba Sonia, Wang, Zenghui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 26.01.2022 MDPI
Témata:	Algorithms Artificial intelligence Automation autonomous moving robot Collaboration Computational linguistics Data mining Electronics industry Emergency procedures Factories Humans Industry Language processing Machine learning Manufacturing Natural language interfaces obstacle-free path planning Q-learning algorithm reinforcement learning (RL) Robotics Robotics industry Robots safety response smart manufacturing Speech Speech Perception Speech recognition software Voice recognition United Kingdom autonomous moving robot smart manufacturing speech recognition obstacle-free path planning Q-learning algorithm reinforcement learning (RL) safety response
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Wang, Zenghui Kiangala, Kahiomba Sonia
AuthorAffiliation	2 Department of Electrical and Mining Engineering, University of South Africa, Johannesburg 1710, South Africa 1 College of Science, Engineering and Technology (CSET), University of South Africa, Johannesburg 1710, South Africa; sokiangala@gmail.com
AuthorAffiliation_xml	– name: 2 Department of Electrical and Mining Engineering, University of South Africa, Johannesburg 1710, South Africa – name: 1 College of Science, Engineering and Technology (CSET), University of South Africa, Johannesburg 1710, South Africa; sokiangala@gmail.com
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SubjectTerms	Algorithms Artificial intelligence Automation autonomous moving robot Collaboration Computational linguistics Data mining Electronics industry Emergency procedures Factories Humans Industry Language processing Machine learning Manufacturing Natural language interfaces obstacle-free path planning Q-learning algorithm reinforcement learning (RL) Robotics Robotics industry Robots safety response smart manufacturing Speech Speech Perception Speech recognition software Voice recognition
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