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Abstract One of the major challenges in 3D printing is its lack of scalability both in size and speed, which directly impacts its economic feasibility for large-scale industrial applications. Cooperative 3D printing (C3DP) is an emerging paradigm that aims to address these issues by employing multiple mobile printers that work in parallel. However, a crucial step in enabling C3DP is the development of a collision-free communication framework between the printers during the manufacturing process. Many C3DP systems found in the literature develop solutions for collision-free printing that are specific to the setup being used, thus not allowing the solution to be transferred to other similar systems. In this paper, we formulate a general framework that generates four distinct collision-free communication strategies to enable arm-arm coordination for C3DP using robotic manipulators. We considered collisions both between the arms with themselves and between the arms and the part being printed. The strategies are general in that they are agnostic to the number of printers, their kinematics, and their spatial configurations in the manufacturing environment. We conducted a study of the four strategies using a two-printer scenario and then physically validated them with four test cases of varying geometries. The results show that the strategies successfully produce printed parts while being collision-free. The makespan reduction using our strategies when compared to a single printer varied from 20% to 42% depending on the strategy used. Finally, we discuss the limitations of the framework, as well as future research directions.
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Network Inference From Local Measurements: Application to Coordination of Groups of Mobile Three-Dimensional Printers
Abstract In recent years, three-dimensional (3D) construction printing has emerged as a viable alternative to conventional construction methods. Particularly promising for large scale construction are collective printing systems consisting of multiple mobile 3D printers. However, the design of these systems typically relies on the assumption of continuous communication between the printers, which is unrealistic in dynamically changing construction environments. As a first step toward decentralized collective 3D printing, we explore an active sensing framework allowing individual agents to reconstruct the shape of the structure, toward assessing other agents' progress in the absence of direct communication. In this vein, the shape of the structure is discretized as a 2D lattice embodying its topology, such that the problem is equivalent to the inference of a network. We leverage environmental modifications introduced by each agent through the printing of new layers to track the structure evolution. We demonstrate the validity of a sequential approach based on system identification through numerical simulations. Our work paves the way to decentralized collective 3D construction printing, as well as other applications in collective behavior that rely on the physical medium to transfer information among agents.
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- Award ID(s):
- 1932187
- PAR ID:
- 10447916
- Date Published:
- Journal Name:
- Journal of Dynamic Systems, Measurement, and Control
- Volume:
- 145
- Issue:
- 1
- ISSN:
- 0022-0434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4056028
