Towards Intelligent Cooperative Robotics in Additive Manufacturing: Past, Present and Future

TL;DR

This article surveys intelligent cooperative robotics in additive manufacturing (C-RAM), focusing on how multiple robotic arms end-effectors can overcome traditional gantry limitations to enable large-scale, multi-arm fabrication with cooperative sensing and multi-material capabilities. It analyzes two core control dimensions—system control (slicing and motion planning) and process control (pre-process, inter-layer, mid-layer), and discusses how sensing and digital twins can support defect mitigation and predictive adjustments. A taxonomy is provided for C-RAM build volumes, segmentation strategies, and collision-avoidance approaches, along with a review of current software ecosystems (e.g., MoveIt, ROS2) and their trade-offs. The paper identifies five critical gaps—unified/informed slicing, intelligent C-RAM systems, informed part decomposition, and digital-twin software—that, when addressed, could unlock robust, scalable, multi-arm AM for large-scale, high-performance components, particularly in metal and CFRTPC domains. The work emphasizes that integrating advanced sensing, real-time feedback, and predictive digital models is essential to translating C-RAM from a promising concept to reliable manufacturing reality.

Abstract

Additive manufacturing (AM) technologies have undergone significant advancements through the integration of cooperative robotics additive manufacturing (C-RAM) platforms. By deploying AM processes on the end effectors of multiple robotic arms, not only are traditional constraints such as limited build volumes circumvented, but systems also achieve accelerated fabrication speeds, cooperative sensing capabilities, and in-situ multi-material deposition. Despite advancements, challenges remain, particularly regarding defect generation including voids, cracks, and residual stress. Various factors contribute to these issues, including toolpath planning (i.e., slicing strategies), part decomposition for cooperative printing, and motion planning (i.e., path and trajectory planning). This review first examines the critical aspects of system control for C-RAM systems comprised of slicing and motion planning. The methods for the mitigation of defects through the adjustment of these aspects and the process parameters of AM methods are then described in the context of how they modify the AM process: pre-process, inter-layer (i.e., during layer pauses), and mid-layer (i.e., during material deposition). The application of advanced sensing technologies, including high-resolution cameras, laser scanners, and thermal imaging, to facilitate the capture of micro, meso, and macro-scale defects is explored. The role of digital twins is analyzed, emphasizing their capability to simulate and predict manufacturing outcomes, enabling preemptive adjustments to prevent defects. Finally, the outlook and future opportunities for developing next-generation C-RAM systems are outlined.

Figures (17)

• Figure 1:
• Figure 2: Flowchart of the review paper which system control methods (i.e., slicing and toolpath planning) and process control methods for defect mitigation across process control levels (pre-process, inter-layer, and mid-layer) and using digital twins.
• Figure 3: Single-arm RAM systems are typically utilized for material extrusion processes such as FFF zhang2023robot (left) and directed energy deposition processes such as wire arc additive manufacturing hassenScalingMetalAdditive2020 (right).
• Figure 4: Multi-arm C-RAM systems offer varying capabilities according to their configuration including a) cooperative sensing capabilities zimermannInprocessNondestructiveEvaluation2023, b) low overlap large scale printing shen_research_2019 and c) high overlap multi-arm fabrication bhattOptimizingMultiRobotPlacements2022.
• Figure 5: Multi-arm C-RAM configurations for a) low overlap (e.g., large-scale fabrication with homogeneous tooling) and b) high overlap (e.g., multi-material fabrication with heterogeneous tooling).