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

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.

Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends

“…While the revolution in the industrial sector is underway, promoting devices, tools, networks and software with broader purposes, the use of PLCs is not yet likely to disappear [78]. PLC-based systems will continue to be required and essential in the majority of solutions within Industry 4.0 [7,79].…”

“…PLCs play an essential role in Smart Manufacturing systems by providing control and interaction with sensors and actuators [80]. Thus, the role of PLCs is evolving from traditional controller tasks to decentralized and integrative functions [78]. Moreover, PLCs are considered IIoT devices, along with communication gateways, enabling connectivity to local networks or the internet for process monitoring and interaction [81].…”

“…Further, in [99], the authors present a system of distributed I/Os based on Raspberry Pi for industrial automation. In [78], voice recognition programmed in the open-source language Python is combined with a Siemens S7-1200 PLC to implement an emergency stop. A conceptual framework for IIoT systems oriented towards smart manufacturing is proposed in [88], which includes PLCs in the network layer, highlighting the coexistence of such devices alongside other IoT technologies.…”

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

“…Another reason for the need for additional publications on AI in SMEs are the very technical case studies to date. Most of them described their approaches and results from an IT expert point of view (Kiangala et al 2022;Lin et al 2021; Manmohan and Shalij 2022). However, since the focus is on SMEs and they usually have less specialized knowledge in their company, especially in the IT area, it is debatable whether the case studies are too complex for many users.…”

Global Models of Smart Cities and Potential IoT Applications: A Review

“…In the event of natural disasters or emergencies, robots can be deployed for search and rescue operations, providing critical assistance to first responders, and minimizing human risk [174]. Despite the challenges, disaster response robotics is a promising field with the potential to save lives and improve the efficiency of disaster response.…”