让移动打印机器人边走边造,自动避障还保精度
Intelligent Navigation and Obstacle-Aware Fabrication for Mobile Additive Manufacturing Systems
- 将导航与打印一体化控制,实时调整路径和动作
- 实测在有障碍和不平地面下仍能保持打印质量
- 适合需要灵活生产、动态调整的智能制造场景
随着大规模定制需求增长,制造系统需更灵活以高效生产个性化产品。增材制造(AM)通过直接从数字模型按需生成定制部件提升了生产灵活性,但其仍受限于固定设备布局。融合移动机器人可解决此问题,使制造资源具备移动性以适应变化需求。移动增材制造机器人(MAMbots)结合了增材制造与移动机器人技术,在动态制造环境中实现部件的现场制造与运输。然而,动态环境带来了新挑战:障碍物与不平地形会干扰导航稳定性,进而影响打印精度与表面质量。本文提出一种通用的移动打印-运输平台,将导航与材料沉积耦合,突破以往分离处理两者的局限。设计了一套实时控制框架,同步规划与控制机器人运动,确保安全行进、避障及路径稳定,同时维持打印质量。通过传感、移动与制造的闭环集成,实现对运动与工艺的实时反馈,使MAMbots能在动态环境中自主决策。框架在仿真与真实实验中验证,具备应对轨迹变化与外部扰动的能力。导航与打印的协同使MAMbots能规划安全、自适应的路径,显著提升制造系统的灵活性与适应性。
原文摘要 · Abstract (English)
As the demand for mass customization increases, manufacturing systems must become more flexible and adaptable to produce personalized products efficiently. Additive manufacturing (AM) enhances production adaptability by enabling on-demand fabrication of customized components directly from digital models, but its flexibility remains constrained by fixed equipment layouts. Integrating mobile robots addresses this limitation by allowing manufacturing resources to move and adapt to changing production requirements. Mobile AM Robots (MAMbots) combine AM with mobile robotics to produce and transport components within dynamic manufacturing environments. However, the dynamic manufacturing environments introduce challenges for MAMbots. Disturbances such as obstacles and uneven terrain can disrupt navigation stability, which in turn affects printing accuracy and surface quality. This work proposes a universal mobile printing-and-delivery platform that couples navigation and material deposition, addressing the limitations of earlier frameworks that treated these processes separately. A real-time control framework is developed to plan and control the robot's navigation, ensuring safe motion, obstacle avoidance, and path stability while maintaining print quality. The closed-loop integration of sensing, mobility, and manufacturing provides real-time feedback for motion and process control, enabling MAMbots to make autonomous decisions in dynamic environments. The framework is validated through simulations and real-world experiments that test its adaptability to trajectory variations and external disturbances. Coupled navigation and printing together enable MAMbots to plan safe, adaptive trajectories, improving flexibility and adaptability in manufacturing.
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