Intelligent Navigation and Obstacle-Aware Fabrication for Mobile Additive Manufacturing Systems

TL;DR

This paper presents a real-time control framework for mobile additive manufacturing robots that integrates navigation and material deposition, enabling autonomous, obstacle-aware fabrication in dynamic environments.

Contribution

It introduces a universal platform coupling navigation and printing, with a control system that ensures stability, obstacle avoidance, and high-quality manufacturing in real-time.

Findings

Successful simulation validation of the control framework.

Real-world experiments demonstrate adaptability to disturbances.

Improved flexibility and safety in mobile additive manufacturing.

Abstract

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.