Autonomous Heavy-Duty Mobile Machinery : A Multidisciplinary Collaborative Challenge
Machado, Tyrone; Fassbender, David; Taheri, Reza; Eriksson, Daniel; Gupta, Himanshu; Molaei, Amirmasoud; Forte, Paolo; Rai, Prashant; Ghabcheloo, Reza; Mäkinen, Saku; Lilienthal, Achim J.; Andreasson, Henrik; Geimer, Marcus (2021-11-05)
Lilienthal, Achim J.
2021 IEEE International Conference on Technology and Entrepreneurship (ICTE)
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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 shortages, 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, HDMMs are complex machines requiring continuous physical and cognitive inputs from human operators. Thus, developing autonomous HDMMs is a huge challenge, with current research and developments fragmented into several independent research domains. Furthermore, autonomous HDMM technologies are a stack of several technologies requiring a convergence of diverse competencies from the different domains. Through this study, we provide an overview of the HDMM industry and use the bounded rationality concept to propose multidisciplinary collaborations for new developments in autonomous HDMMs. Furthermore, we apply the transaction cost economics framework to highlight the conceptual challenges and implications of these collaborations. Therefore, we bring together several domains of the HDMM industry to introduce autonomous HDMMs as a general and unified approach. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks. In doing so, we highlight the need for new and multidisciplinary perspectives that should be considered by academic and industrial practitioners working on the development and deployment of autonomous HDMMs.
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