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.

翻译：高效的物料物流在控制建筑行业的成本与进度中扮演关键角色。然而，人工物料搬运仍易出现效率低下、延误及安全风险。自主式叉车为简化现场物流、减少对人工操作员的依赖并缓解劳动力短缺提供了有前景的解决方案。本文介绍了ADAPT（自主动态全地形托盘运输车）的开发与评估，这是一款专为建筑环境设计的全自主越野叉车。与结构化的仓库环境不同，建筑工地面临动态障碍、非结构化地形及多变天气条件等重大挑战。为应对这些挑战，本系统将基于AI的感知技术与传统方法相结合，用于决策、规划与控制，从而在复杂环境中实现可靠运行。我们通过广泛的实际测试对系统进行了验证，并在各种天气条件下将其连续性能与经验丰富的人工操作员进行了对比。研究结果表明，自主户外叉车能够达到接近人类水平的运行性能，为更安全、更高效的建筑物流提供了可行路径。