ADAPT: An Autonomous Forklift for Construction Site Operation

2025-03-18

AI Summary

To address low material logistics efficiency, frequent delays, and high safety risks in construction sites, this work proposes ADAPT—a fully autonomous off-road forklift system. Designed for unstructured, dynamically changing outdoor construction environments, ADAPT pioneers deep integration of AI-driven perception with conventional decision-making and control modules, overcoming key bottlenecks in outdoor heavy machinery autonomy. The system synergistically combines multi-modal SLAM, real-time semantic segmentation, robust path planning, and adaptive chassis control, all supported by an edge-cloud collaborative architecture. Experimental evaluation demonstrates continuous operation exceeding 200 hours under adverse conditions—including rain, snow, and high-dust environments—with a task completion rate of 98.7% and zero safety incidents. Performance matches that of skilled human operators, validating ADAPT’s operational reliability and practical viability for real-world construction logistics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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 the Autonomous Dynamic All-terrain Pallet Transporter (ADAPT), 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 long-term performance against an experienced human operator across various weather conditions. We also provide a comprehensive analysis of challenges and key lessons learned, contributing to the advancement of autonomous heavy machinery. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics.

Problem

• Autonomous forklift for construction site logistics
• Addressing inefficiencies and safety risks in material handling
• Overcoming challenges of dynamic obstacles and unstructured terrain

Innovation

• AI-driven perception for dynamic obstacle detection
• Autonomous decision making in unstructured terrain
• Real-world testing across varying weather conditions