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A Data-Driven Method for Inserting Anchor Bolts into Concrete Holes


Core Concepts
A data-driven method using deep reinforcement learning enables an industrial robot to effectively find and insert anchor bolts into variable-shaped holes in concrete.
Abstract
The proposed method addresses the challenges of automating anchor bolt insertion in concrete, which is a common task in the construction industry. Unlike typical peg-in-hole tasks, the holes in concrete have variable shape and surface finish due to the brittle nature of the material, making it difficult to apply analytical modeling or control parameter tuning approaches. The key aspects of the proposed method are: A hole-search strategy that involves slightly detaching the peg from the concrete surface between search positions to avoid problems related to the high friction coefficient of concrete. The adoption of a deep neural network (DNN) trained via reinforcement learning to find holes with variable surface finish, without the need for analytical modeling. Improved generalization capabilities of the DNN by introducing the displacement of the peg (in addition to force and torque) as an input. The ability to find holes without relying on the absolute position of the peg as an input, enabling generalization to different hole positions. The method was evaluated through experiments using a robotic setup similar to construction sites. The results demonstrate the effectiveness of the proposed approach, with an average success rate of 96.1% and average execution time of 12.5 seconds for finding unknown holes. Additional evaluations with random initial positions and a different type of peg show the trained DNN can generalize well to different conditions.
Stats
The peg displacement on the Z-axis (Dz) increases as the peg gets closer to the hole due to the chamfered shape of the holes in concrete. The average success rate of the proposed method is 96.1% for finding unknown holes. The average execution time of the proposed method is 12.5 seconds for finding unknown holes.
Quotes
"The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete's high friction coefficient." "The method uses displacement of the peg toward the wall surface, in addition to force and torque, as one of the inputs of the DNN. Since the displacement increases as the peg gets closer to the hole (due to the chamfered shape of holes in concrete), it is a useful parameter for inputting in the DNN."

Key Insights Distilled From

by Andr... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19946.pdf
A Peg-in-hole Task Strategy for Holes in Concrete

Deeper Inquiries

How could the proposed method be extended to handle more complex hole shapes and surface conditions, such as those found in real-world construction sites

The proposed method could be extended to handle more complex hole shapes and surface conditions by incorporating advanced sensor technologies and adaptive learning algorithms. To address the challenges posed by varying hole shapes and surface conditions in real-world construction sites, the system could integrate 3D vision systems to accurately detect and analyze the geometry of the holes. This data could then be used to train the deep neural network (DNN) to recognize and adapt to different hole shapes and surface finishes. Additionally, the system could implement a feedback mechanism that adjusts the search strategy based on real-time sensor data, allowing the robot to dynamically respond to changes in the environment. By continuously learning and adapting to new hole configurations, the system can improve its performance and effectiveness in handling complex scenarios.

What are the potential limitations of the deep reinforcement learning approach used in this work, and how could they be addressed in future research

The deep reinforcement learning approach used in this work may have potential limitations, such as sample inefficiency, exploration-exploitation trade-offs, and generalization to unseen scenarios. To address these limitations in future research, one approach could be to incorporate transfer learning techniques to leverage knowledge gained from training on simpler tasks to improve performance on more complex tasks. Additionally, ensemble learning methods could be employed to combine multiple DNN models to enhance robustness and reduce overfitting. Implementing curriculum learning strategies, where the difficulty of the training tasks gradually increases, can also help the DNN adapt to a wider range of scenarios. Furthermore, incorporating human feedback or demonstrations into the training process can provide valuable insights and guidance to improve the learning efficiency and effectiveness of the DNN.

What other robotic tasks in the construction industry could benefit from a similar data-driven, learning-based approach, and how would the implementation differ from the anchor bolt insertion problem

Other robotic tasks in the construction industry that could benefit from a similar data-driven, learning-based approach include material handling, site inspection, and autonomous equipment operation. For material handling tasks, such as sorting and transporting construction materials, the DNN could be trained to identify and manipulate objects of varying shapes and sizes. Site inspection tasks, such as detecting structural defects or monitoring progress, could utilize the DNN to analyze visual data and make informed decisions. Autonomous equipment operation, such as excavators or cranes, could benefit from learning-based control algorithms to optimize movement and operation efficiency. The implementation for these tasks would involve collecting relevant data, defining appropriate actions, and training the DNN to perform the tasks autonomously while adapting to changing conditions in the construction environment.
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