基于ISO3691—2024的无人驾驶叉车避障算法与安全验证框架研究

Research on Obstacle Avoidance Algorithms and Safety Verification Framework for Autonomous Forklifts Based on ISO 3691—2024

摘要:

文中聚焦无人驾驶叉车在复杂动态工业环境中的运行挑战,探讨了其关键实时避障算法原理,并依据ISO 3691-2024《工业车辆安全》中对无人驾驶工业车辆的要求,提出了避障算法功能与性能的符合性验证框架;分析了激光雷达、视觉、超声波等多传感器融合策略,阐述了基于环境感知的动态路径重规划与安全逻辑;构建了衔接算法工程与安全法规的验证体系,涵盖感知可靠性、决策合理性、执行响应及功能安全完整性评估,为无人叉车的安全部署与检验认证提供理论与实践依据。本研究对提升无人驾驶叉车在真实工况下的鲁棒性、安全性与标准符合性具有重要指导意义。

Abstract:

This paper focuses on the operational challenges of autonomous forklifts in complex and dynamic industrial environments. It explores the principles of key real-time obstacle avoidance algorithms and proposes a compliance verification framework for the functionality and performance of such algorithms based on the requirements for autonomous industrial vehicles specified in ISO 3691—2024 Industrial trucks—Safety requirements and verification. It analyzes multi-sensor fusion strategies involving LiDAR, vision, ultrasonic, and other technologies, and describes dynamic path replanning and safety logic based on environmental perception. A verification system bridging algorithmic engineering and safety regulations has been established, covering perception reliability, decision-making rationality, actuation response, and functional safety integrity assessment. This system provides both theoretical and practical foundations for the safe deployment, inspection, and certification of autonomous forklifts. This study offers significant guidance for improving the robustness, safety, and regulatory compliance of autonomous forklifts under real-world operating conditions.