Continuous Mobile Thin-Layer On-Site Printing

Jenny, Selen Ercan; Pietrasik, Lukasz L.; Sounigo, Eliott; Tsai, Ping-Hsun; Gramazio, Fabio; Köhler, Matthias; Lloret-Fritschi, Ena; Hutter, Marco

doi:10.1016/j.autcon.2022.104634

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…The area of trajectory generation and path planning for robotic plastering is less covered in existing literature, especially for trowel-based plastering. Methods have been proposed for automatically adapting trajectories to the current state of the work surface [12], [13] and for an operator to interactively define trajectories in an augmented reality setting [14]. However, both of these works only consider plaster spraying.…”

Section: A Related Workmentioning

confidence: 99%

PLASTR: Planning for Autonomous Sampling-Based Trowelling

Kuhlmann-Jørgensen,

Pankert,

Pietrasik

et al. 2023

IEEE Robot. Autom. Lett.

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Plaster is commonly used in the construction industry to finish walls and ceilings, but the application is labor-intensive and physically strenuous, which motivates the need for automation. We present PLASTR, a receding horizon optimization-based planning algorithm for robotic plaster trowelling. It samples trowelling sequence rollouts from a new plaster simulator and weights them according to the flatness of the finished wall. The proposed simulator approximates the real-world plastertrowel interaction adequately while allowing execution orders of magnitude faster than real-time. We evaluate PLASTR in simulation and on a real-world test setup and compare it to two handcrafted heuristic baseline algorithms. PLASTR performs equal to or better than the best heuristic in terms of material coverage for both simulated and real-world experiments while being 50 % more efficient in terms of trowelled distance.

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Section: A Related Workmentioning

confidence: 99%

PLASTR: Planning for Autonomous Sampling-Based Trowelling

Kuhlmann-Jørgensen,

Pankert,

Pietrasik

et al. 2023

IEEE Robot. Autom. Lett.

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“…Currently, most robotic arms are fixed in a specific position to operate and their applications are limited to repetitive work in industrial production [1,2]. With the expansion of the robot application field, the environment faced by the robot is increasingly diversified, and the operation performed has a variety of uncertainties, which requires the robot to have the ability to move while also having specific operation abilities [3]. Therefore, the design of a mobile robotic arm can meet this demand.…”

Section: Introductionmentioning

confidence: 99%

Research on Positioning and Simulation Method for Autonomous Mobile Construction Platform

Shi,

Wang,

Phillips

et al. 2024

Buildings

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In the architecture, engineering, and construction (AEC) industry, the positioning technology for a mobile construction platform (MCP) is critical to achieve on-site, continuous, large-scale construction. During construction, MCP movement and construction actions seldom occur simultaneously. Therefore, this paper categorizes the MCP into stationary and moving states for positioning studies, respectively. When the platform is stationary, it is positioned using an improved ultra-wideband (UWB) sensor. When the platform is in motion, a single UWB positioning technique cannot meet the required accuracy for positioning, and fusion positioning using both UWB and an inertial measurement unit (IMU) is considered. The experimental results show that compared with only UWB positioning, the improved UWB positioning algorithm improves the positioning accuracy by 53% in the stationary state, and the fused UWB/IMU positioning improves the positioning accuracy by 46% in the moving state. As a result, the positioning accuracy of the MCP is significantly improved regardless of whether it is in a stationary or moving state. Furthermore, this paper integrates the positioning technique with the robotic arm construction technique to successfully simulate an on-site continuous construction of a wooden cabin, which provides the potential for large-scale continuous construction in real-world scenarios in the future.

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“…Path-continuous tasks involve a predefined spatial path, whereas trajectory-continuous tasks require the robot to follow a time-parametrized path viz. 3D printing [3,9], writing [10], spraying [11] etc. The problem of finding optimum sequence to complete the task has been solved by clustering [7] and optimum path planning is achieved [3].…”

Section: Introductionmentioning

confidence: 99%

Example-Driven Trajectory Learner for Robots under Structured Static Environment

S.,

Kamal,

Kurian

2023

Preprint

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With the breakthroughs in machine learning and computing infrastructures that have led to significant performance improvements in cognitive robotics, the challenge of trajectory-continuous task creation persists. Various constraints in the physical capability of robots, environmental changes and long-time reliance on sequential dependencies between inter-joint and intra-joint relationships made the work exceptionally hard. Many robot environments function under structured static work-cell completing extended series of subtasks. The conventional descriptors for robot trajectory rely on symbolic rules with human intelligence, which involves skilled individuals and possesses significant limitations, such as being time-consuming and requiring enhanced adaptability due to the static nature of task descriptions alone.On the other hand, reinforcement learning is an empiricism-based approach that learns through iterative interaction with the environment. However, the resource requirements for achieving convergence and the need for appropriate infrastructure can be substantial, especially in complex environments with a large action space that can pose significant challenges. Artificially inculcating innate prior knowledge is introduced with a dataset to reduce the search space in the symbolic trajectory learner.The suggested technique employs a probabilistic network and data-efficient modelling termed generative adversarial networks, which learns the underlying constraints, probability distributions and arbitrations, along with generating a representation of trajectory instances at each time of sampling. This research also proposes a way to calculate the robot path accuracy in extrinsic generative models. The model assessment was carried out by utilising a custom-built dataset and robot operating system, yielding encouraging results in robot path accuracy and generated samples.

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Continuous Mobile Thin-Layer On-Site Printing

Cited by 14 publications

References 27 publications

PLASTR: Planning for Autonomous Sampling-Based Trowelling

PLASTR: Planning for Autonomous Sampling-Based Trowelling

Research on Positioning and Simulation Method for Autonomous Mobile Construction Platform

Example-Driven Trajectory Learner for Robots under Structured Static Environment

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