The safety of open hardware robots (AGVs) in factories
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
The use of Automated Guided Vehicles (AGVs) has become an important part of logistics transportation systems in various factories and industries. Due to their advanced intelligence and automation capabilities, the use of AGVs in the industry is increasing. However, their use in small industries has been limited due to high costs and lack of flexibility. To address this issue, the concept of creating open-source hardware AGVs has emerged. Open-source AGVs can allow smaller industries to customize and scale their automation solutions. By leveraging open-source innovation, these industries can enhance the effectiveness, adaptability, and cost efficiency of their automation processes. With this in mind, the safety of OSH AGVs in industrial settings was investigated in this research. The investigations revealed that building open-source AGVs is indeed possible. The advantages of developing AGV-OSH include economic gains, adaptability, cooperative efforts, heightened creativity, the availability of sophisticated customizable software, cost curtailment, and sustainability enhancement, especially for smaller industries. However, there are various safety concerns and challenges in the construction of AGV-OSH. These include the design approval process and evaluation tests, manufacturing quality control process of AGV-OSH, user expertise, compliance with regulations, responsibility, accountability, security concerns, and changes in the application. Reducing safety concerns for OSH AGVs involves developing robust safety mechanisms to manage updates and modifications while ensuring compliance with the latest safety standards. Advanced virtual testing methods, real-time monitoring, and adaptive algorithms on platforms like Raspberry Pi can enhance safety by dynamically adjusting to environmental changes. Implementing robust maintenance protocols and integrating machine learning algorithms, such as Multi-Agent Reinforcement Learning, can further optimize safety and operational efficiency. Additionally, complying with established safety standards like ANSI/ITSDF B56.5-2019 and EN ISO 3691-4:2020 ensures that AGVs meet necessary safety requirements while preserving the flexibility and collaborative spirit of open-source projects. By tackling current challenges, following relevant standards, and adhering to licensing requirements, industries can develop cost-effective, customizable, and safe open-source hardware AGVs.
Place, publisher, year, edition, pages
2024. , p. vii, 73
Keywords [en]
AGV, open source hardware, safety, AGV-OSH
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:his:diva-24805OAI: oai:DiVA.org:his-24805DiVA, id: diva2:1923162
Subject / course
Virtual Product Realization
Educational program
Intelligent Automation - Master's Programme, 120 ECTS
Supervisors
Examiners
2024-12-202024-12-202025-09-29Bibliographically approved