Autonomous Heavy-Duty Mobile Machinery: A Multidisciplinary Collaborative Challenge
Tyrone Machado, David Fassbender, Abdolreza Taheri, Daniel Eriksson,, Himanshu Gupta, Amirmasoud Molaei, Paolo Forte, Prashant Rai, Reza, Ghabcheloo, Saku M\"akinen, Achim Lilienthal, Henrik Andreasson, Marcus, Geimer

TL;DR
This paper discusses the complexities and multidisciplinary collaboration needed to develop autonomous heavy-duty mobile machinery, addressing challenges like safety, labor shortages, and technical integration.
Contribution
It proposes a multidisciplinary collaboration framework using bounded rationality and transaction cost economics for autonomous HDMM development.
Findings
Identifies key collaborative domains including AI, sensors, and regulation.
Highlights practical challenges in multidisciplinary integration.
Suggests future industry implications and research directions.
Abstract
Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human-operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future…
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