Abstract
Aerial 3D printing is a pioneering technology yet in its conceptual stage that combines frontiers of 3D printing and Unmanned aerial vehicles (UAVs) aiming to construct large-scale structures in remote and hard-to-reach locations autonomously. The envisioned technology will enable a paradigm shift in the construction and manufacturing industries by utilizing UAVs as precision flying construction workers. However, the limited payload-carrying capacity of the UAVs, along with the intricate dexterity required for manipulation and planning, imposes a formidable barrier to overcome. Aiming to surpass these issues, a novel aerial decomposition-based and scheduling 3D printing framework is presented in this article, which considers a near-optimal decomposition of the original 3D shape of the model into smaller, more manageable sub-parts called chunks. This is achieved by searching for planar cuts based on a heuristic function incorporating necessary constraints associated with the interconnectivity between subparts, while avoiding any possibility of collision between the UAV’s extruder and generated chunks. Additionally, an autonomous task allocation framework is presented, which determines a priority-based sequence to assign each printable chunk to a UAV for manufacturing. The efficacy of the proposed framework is demonstrated using the physics-based Gazebo simulation engine, where various primitive CAD-based aerial 3D constructions are established, accounting for the nonlinear UAVs dynamics, associated motion planning and reactive navigation through Model predictive control.
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Karasik, E., Fattal, R., Werman, M.: Object partitioning for support-free 3d-printing. In: Computer Graphics Forum, vol. 38, pp. 305–316. Wiley Online Library (2019)
Balletti, C., Ballarin, M., Guerra, F.: 3d printing: state of the art and future perspectives. J. Cult. Herit. 26, 172–182 (2017)
Zuniga, J., Katsavelis, D., Peck, J., Stollberg, J., Petrykowski, M., Carson, A., Fernandez, C.: Cyborg beast: a low-cost 3d-printed prosthetic hand for children with upper-limb differences. BMC Res. Notes 8(1), 1–9 (2015)
Pearce, J.M.: Applications of open source 3-d printing on small farms. Organic Farming 1(1) (2015)
Craveiroa, F., Duartec, J.P., Bartoloa, H., Bartolod, P.J.: Additive manufacturing as an enabling technology for digital construction: A perspective on construction 4.0. Sustain. Dev. 4(6) (2019)
Joshi, S.C., Sheikh, A.A.: 3d printing in aerospace and its long-term sustainability. Virtual Phys. Prototyp. 10(4), 175–185 (2015)
Al Jassmi, H., Al Najjar, F., Mourad, A.-H.I.: Large-scale 3d printing: the way forward. In: IOP Conference Series: Materials Science and Engineering, vol. 324, p. 012088. IOP Publishing (2018)
Bazli, M., Ashrafi, H., Rajabipour, A., Kutay, C.: 3d printing for remote housing: benefits and challenges. Autom. Constr. 148, 104772 (2023)
Xu, Z., Song, T., Guo, S., Peng, J., Zeng, L., Zhu, M.: Robotics technologies aided for 3d printing in construction: a review. Int. J. Adv. Manuf. Technol. 118(11–12), 3559–3574 (2022)
Zhang, K., Chermprayong, P., Xiao, F., Tzoumanikas, D., Dams, B., Kay, S., Kocer, B.B., Burns, A., Orr, L., Choi, C., et al.: Aerial additive manufacturing with multiple autonomous robots. Nature 609(7928), 709–717 (2022)
Goessens, S., Mueller, C., Latteur, P.: Feasibility study for drone-based masonry construction of real-scale structures. Autom. Constr. 94, 458–480 (2018)
Hunt, G., Mitzalis, F., Alhinai, T., Hooper, P.A., Kovac, M.: 3d printing with flying robots. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4493–4499. IEEE (2014)
Jiang, J., Newman, S.T., Zhong, R.Y.: A review of multiple degrees of freedom for additive manufacturing machines. Int. J. Comput. Integr. Manuf. 34(2), 195–211 (2021)
Ruan, J., Sparks, T.E., Panackal, A., Liou, F.W., Eiamsa-Ard, K., Slattery, K., Chou, H.-N., Kinsella, M.: Automated slicing for a multiaxis metal deposition system. (2007)
Luo, L., Baran, I., Rusinkiewicz, S., Matusik, W.: Chopper: partitioning models into 3d-printable parts. ACM Trans. Graph. (TOG) 31(6), 1–9 (2012)
Gao, Y., Wu, L., Yan, D.-M., Nan, L.: Near support-free multi-directional 3d printing via global-optimal decomposition. Graph. Model 104, 101034 (2019)
Poudel, L., Marques, L.G., Williams, R.A., Hyden, Z., Guerra, P., Fowler, O.L., Sha, Z., Zhou, W.: Toward swarm manufacturing: architecting a cooperative 3d printing system. J. Manuf. Sci. Eng. Trans. ASME 144(8), 1–15 (2022)
Fuchs, H., Kedem, Z.M., Naylor, B.F.: On visible surface generation by a priori tree structures. SIGGRAPH Comput. Graph. 14(3), 124–133 (1980)
McPherson, J., Zhou, W.: A chunk-based slicer for cooperative 3D printing. Rapid Prototyp. J. 24(9), 1436–1446 (2018)
Fazzini, G., Paolini, P., Paolucci, R., Chiulli, D., Barile, G., Leoni, A., Muttillo, M., Pantoli, L., Ferri, G.: Print on air: Fdm 3d printing without supports. In: 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0IoT), pp. 350–354 (2019)
Ultimaker B.V.: Ultimaker Cura. https://ultimaker.com/software/ultimaker-cura
Lindqvist, B., Mansouri, S.S., Sopasakis, P., Nikolakopoulos, G.: Collision avoidance for multiple micro aerial vehicles using fast centralized nonlinear model predictive control. IFAC-PapersOnLine. 53(2), 9303–9309 (2020). 21st IFAC World Congress
Sathya, A., Sopasakis, P., Van Parys, R., Themelis, A., Pipeleers, G., Patrinos, P.: Embedded nonlinear model predictive control for obstacle avoidance using panoc. In: 2018 European Control Conference (ECC), pp. 1523–1528. IEEE (2018)
Sopasakis, P., Fresk, E., Patrinos, P.: Open: code generation for embedded nonconvex optimization. IFAC-PapersOnLine. 53(2), 6548–6554 (2020). 21st IFAC World Congress
Andersson, J.A., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: Casadi: a software framework for nonlinear optimization and optimal control. Math. Program. Comput. 11, 1–36 (2019)
Stella, L., Themelis, A., Sopasakis, P., Patrinos, P.: A simple and efficient algorithm for nonlinear model predictive control. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 1939–1944. IEEE (2017)
Lindqvist, B., Mansouri, S.S., Haluška, J., Nikolakopoulos, G.: Reactive navigation of an unmanned aerial vehicle with perception-based obstacle avoidance constraints. IEEE Trans. Control. Syst. Technol. 30(5), 1847–1862 (2022)
Furrer, F., Burri, M., Achtelik, M., Siegwart, R.: RotorS–A Modular Gazebo MAV Simulator Framework. In: Koubaa, A. (ed.) Robot Operating System (ROS): The Complete Reference (vol. 1), pp. 595–625. Springer, Cham (2016)
Wuthier, D., Kominiak, D., Kanellakis, C., Andrikopoulos, G., Fumagalli, M., Schipper, G., Nikolakopoulos, G.: On the design, modeling and control of a novel compact aerial manipulator. In: 2016 24th Mediterranean Conference on Control and Automation (MED) (2016)
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Marios-Nektarios Stamatopoulos: Development, implementation, system integration and simulation, relating to all presented sub-modules and developments, main manuscript contributor. Avijit Banerjee: Advisory, research guidance, field expertise, manuscript contributions. George Nikolakopoulos: Advisory, manuscript contributions, head of Luleå University of Technology Robotics & Artificial Intelligence Team. All authors have read and approved the manuscript.
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Stamatopoulos, MN., Banerjee, A. & Nikolakopoulos, G. A Decomposition and a Scheduling Framework for Enabling Aerial 3D Printing. J Intell Robot Syst 110, 53 (2024). https://doi.org/10.1007/s10846-024-02081-8
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DOI: https://doi.org/10.1007/s10846-024-02081-8