ADAPT让叉车在工地自主运货，表现接近人类司机。
ADAPT: An Autonomous Forklift for Construction Site Operation
- 融合AI感知与传统控制，应对工地复杂环境。
- 实测显示其性能接近经验丰富的操作员。
- 适合需要高效安全物流的建筑工地使用。
高效的物料物流对控制建筑行业成本和工期至关重要。然而，人工搬运仍易导致效率低下、延误和安全风险。自主叉车为改善现场物流提供了可行方案，可减少对人力的依赖并缓解劳动力短缺问题。本文介绍了针对施工环境设计的全自主非公路叉车ADAPT（Autonomous Dynamic All-terrain Pallet Transporter）的开发与评估。相较于结构化的仓库场景，施工现场面临动态障碍物、非结构化地形及多变天气等挑战。为此，系统结合了基于AI的感知技术与传统决策、规划和控制方法，实现复杂环境下的可靠运行。通过大量真实场景测试，将系统连续表现与经验丰富的操作员进行对比，验证了其在不同天气条件下的有效性。结果表明，自主室外叉车可达到接近人类水平的表现，为更安全、高效的建筑物流提供了可行路径。
原文摘要 · Abstract (English)
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.
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