Mobile robotics and 3D printing: addressing challenges in path planning and scalability

Abstract

Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.