ADAPT: An Autonomous Forklift for Construction Site Operation
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908345502269440 |
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| author | Huemer, Johannes Murschitz, Markus Schörghuber, Matthias Reisinger, Lukas Kadiofsky, Thomas Weidinger, Christoph Niedermeyer, Mario Widy, Benedikt Zeilinger, Marcel Beleznai, Csaba Glück, Tobias Kugi, Andreas Zips, Patrik |
| author_facet | Huemer, Johannes Murschitz, Markus Schörghuber, Matthias Reisinger, Lukas Kadiofsky, Thomas Weidinger, Christoph Niedermeyer, Mario Widy, Benedikt Zeilinger, Marcel Beleznai, Csaba Glück, Tobias Kugi, Andreas Zips, Patrik |
| contents | Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of ADAPT (Autonomous Dynamic All-terrain Pallet Transporter), a fully autonomous off-road forklift designed for construction environments. Unlike structured warehouse settings, construction sites pose significant challenges, including dynamic obstacles, unstructured terrain, and varying weather conditions. To address these challenges, our system integrates AI-driven perception techniques with traditional approaches for decision making, planning, and control, enabling reliable operation in complex environments. We validate the system through extensive real-world testing, comparing its continuous performance against an experienced human operator across various weather conditions. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ADAPT: An Autonomous Forklift for Construction Site Operation Huemer, Johannes Murschitz, Markus Schörghuber, Matthias Reisinger, Lukas Kadiofsky, Thomas Weidinger, Christoph Niedermeyer, Mario Widy, Benedikt Zeilinger, Marcel Beleznai, Csaba Glück, Tobias Kugi, Andreas Zips, Patrik Robotics Computer Vision and Pattern Recognition Systems and Control Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of ADAPT (Autonomous Dynamic All-terrain Pallet Transporter), a fully autonomous off-road forklift designed for construction environments. Unlike structured warehouse settings, construction sites pose significant challenges, including dynamic obstacles, unstructured terrain, and varying weather conditions. To address these challenges, our system integrates AI-driven perception techniques with traditional approaches for decision making, planning, and control, enabling reliable operation in complex environments. We validate the system through extensive real-world testing, comparing its continuous performance against an experienced human operator across various weather conditions. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics. |
| title | ADAPT: An Autonomous Forklift for Construction Site Operation |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2503.14331 |