insight - Robotics, Construction - # Automated structural part fixation to concrete surfaces

Dual-Arm Robot System for Automated Fixation of Structural Parts to Concrete Surfaces in Narrow Construction Environments

Q: How could the proposed system be further improved to increase the speed and efficiency of the structural part fixation process?

To enhance the speed and efficiency of the structural part fixation process, several improvements can be implemented in the proposed system. Firstly, optimizing the tool changing mechanism to reduce the time taken for tool attachment and detachment can significantly improve the overall process efficiency. Implementing a more streamlined and automated tool changing system can minimize downtime between tasks. Furthermore, integrating real-time monitoring and feedback mechanisms can enhance the system's performance. By incorporating sensors that provide feedback on the drilling depth, anchor insertion force, and nut tightening torque, the robots can adjust their actions dynamically, ensuring precise and accurate fixation of structural parts. Additionally, refining the drilling and anchoring algorithms based on machine learning techniques can optimize the path planning and execution of these tasks. By training the robots to adapt to different surface conditions and variations in structural part dimensions, the system can operate more efficiently and effectively. Moreover, exploring parallel processing capabilities by enabling both robots to work simultaneously on different parts of the structural assembly can significantly reduce the overall fixation time. By coordinating the actions of the two arms efficiently, the system can achieve a higher throughput and faster completion of the fixation process.

Q: What are the potential challenges in scaling up the proposed system to handle larger or more complex structural parts in construction environments?

Scaling up the proposed system to handle larger or more complex structural parts in construction environments may pose several challenges. One significant challenge is the increased payload and reach requirements for larger structural parts. Larger parts may necessitate stronger robots with higher payload capacities and longer reach capabilities, which could impact the overall system design and cost. Moreover, the complexity of the structural parts may require more sophisticated tool designs and control algorithms to ensure accurate and secure fixation. Ensuring that the robots can handle the weight and dimensions of larger parts without compromising precision and safety is crucial in scaling up the system. Additionally, navigating and operating in confined or complex construction environments with larger structural parts can be challenging. The robots must be able to maneuver effectively in tight spaces, avoid obstacles, and adapt to changing conditions while handling bulkier components. Furthermore, the integration of advanced sensing and control technologies may be more complex for larger and more intricate structural parts. Ensuring seamless communication and coordination between the robots, tools, and sensors becomes increasingly critical as the system scales up to handle more demanding tasks.

Q: How could the integration of advanced sensing and control technologies, such as machine learning-based perception and planning, enhance the robustness and adaptability of the proposed system?

Integrating advanced sensing and control technologies, such as machine learning-based perception and planning, can significantly enhance the robustness and adaptability of the proposed system. By leveraging machine learning algorithms for perception tasks, the robots can improve their ability to recognize and interact with structural parts accurately. This can include identifying specific features, detecting anomalies, and adjusting their actions based on real-time feedback. Machine learning-based planning algorithms can optimize the path planning and task sequencing for the robots, enabling them to perform tasks more efficiently and adapt to changing environments. By learning from past experiences and continuously improving their decision-making processes, the robots can enhance their overall performance and adaptability. Furthermore, advanced sensing technologies, such as 3D cameras and LiDAR sensors, can provide the robots with detailed spatial information about their surroundings, enabling them to navigate complex construction environments more effectively. This enhanced perception capability can improve the robots' ability to avoid collisions, plan optimal paths, and interact with structural parts with greater precision. Overall, the integration of these advanced technologies can enhance the system's overall performance, making it more robust, adaptable, and capable of handling a wider range of construction tasks with efficiency and accuracy.

Core Concepts

A dual-arm robotic system with custom tools enables the complete automated installation of structural parts to concrete surfaces in narrow construction environments.

Abstract

The study proposes a robotic system for the automated fixation of structural parts, such as mechanical, electrical, and plumbing (MEP) support systems, to concrete surfaces. The system consists of two industrial robots, each equipped with specialized tools for drilling holes, inserting anchor bolts, and tightening nuts.

Key highlights:

Dual-arm design allows the robots to position and fix the structural parts simultaneously, enabling complete automation of the installation process.
Custom tool designs, including a moment-compensating drill and an anchor hammering tool with an integrated gripper, enable the use of smaller robots with lower payloads in narrow construction environments.
A modular system design allows the robots and tools to be transported in parts for easy introduction to the construction site.
A detailed fixation procedure is proposed, which includes steps for detecting the wall orientation, locating the structural part holes, drilling holes, inserting anchors, and tightening nuts.

Experimental results demonstrate the feasibility of the proposed system and tools. The custom drill tool with a constant load spring successfully drilled holes without causing moment overload in the robot joints. The anchor hammering and nut tightening tools also performed their tasks within the robot's force and torque limits. The complete fixation procedure was evaluated, and one side of a structural part was successfully installed in under 10 minutes.

The proposed system and tools show potential for automating construction tasks in narrow and unstructured environments, addressing the labor shortage and skill gap in the construction industry.

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Stats

The robots used in the experimental setup have a length of 1110 mm, a reach of 1298 mm, and a payload of 13 kg.
The concrete block used has a size of H300xW200xT150 mm and a compressive strength of 24 N/mm2.
The anchor bolt used has a diameter of 12 mm and a length of 126 mm, with a mass of 113 g.

Quotes

"The use of two arms to automate assembly tasks in construction is not new; a robotic systems was previously developed for installing ceiling boards and raised floors in construction sites."
"To avoid this, efforts have been made to automate construction tasks using industrial robots, as they are suitable for tasks that are dull, repetitive, and require high precision."
"The proposed design is similar to the offset axis approach, but it contains a support arm that compensates for the moment generated by drilling."

Key Insights Distilled From

Dual-Arm Construction Robot for Automatic Fixation of Structural Parts to Concrete Surfaces in Narrow Environments

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

https://arxiv.org/pdf/2403.19948.pdf

Dual-Arm Construction Robot for Automatic Fixation of Structural Parts to Concrete Surfaces in Narrow Environments

Deeper Inquiries

How could the proposed system be further improved to increase the speed and efficiency of the structural part fixation process?

To enhance the speed and efficiency of the structural part fixation process, several improvements can be implemented in the proposed system. Firstly, optimizing the tool changing mechanism to reduce the time taken for tool attachment and detachment can significantly improve the overall process efficiency. Implementing a more streamlined and automated tool changing system can minimize downtime between tasks.
Furthermore, integrating real-time monitoring and feedback mechanisms can enhance the system's performance. By incorporating sensors that provide feedback on the drilling depth, anchor insertion force, and nut tightening torque, the robots can adjust their actions dynamically, ensuring precise and accurate fixation of structural parts.
Additionally, refining the drilling and anchoring algorithms based on machine learning techniques can optimize the path planning and execution of these tasks. By training the robots to adapt to different surface conditions and variations in structural part dimensions, the system can operate more efficiently and effectively.
Moreover, exploring parallel processing capabilities by enabling both robots to work simultaneously on different parts of the structural assembly can significantly reduce the overall fixation time. By coordinating the actions of the two arms efficiently, the system can achieve a higher throughput and faster completion of the fixation process.

What are the potential challenges in scaling up the proposed system to handle larger or more complex structural parts in construction environments?

Scaling up the proposed system to handle larger or more complex structural parts in construction environments may pose several challenges. One significant challenge is the increased payload and reach requirements for larger structural parts. Larger parts may necessitate stronger robots with higher payload capacities and longer reach capabilities, which could impact the overall system design and cost.
Moreover, the complexity of the structural parts may require more sophisticated tool designs and control algorithms to ensure accurate and secure fixation. Ensuring that the robots can handle the weight and dimensions of larger parts without compromising precision and safety is crucial in scaling up the system.
Additionally, navigating and operating in confined or complex construction environments with larger structural parts can be challenging. The robots must be able to maneuver effectively in tight spaces, avoid obstacles, and adapt to changing conditions while handling bulkier components.
Furthermore, the integration of advanced sensing and control technologies may be more complex for larger and more intricate structural parts. Ensuring seamless communication and coordination between the robots, tools, and sensors becomes increasingly critical as the system scales up to handle more demanding tasks.

How could the integration of advanced sensing and control technologies, such as machine learning-based perception and planning, enhance the robustness and adaptability of the proposed system?

Integrating advanced sensing and control technologies, such as machine learning-based perception and planning, can significantly enhance the robustness and adaptability of the proposed system. By leveraging machine learning algorithms for perception tasks, the robots can improve their ability to recognize and interact with structural parts accurately. This can include identifying specific features, detecting anomalies, and adjusting their actions based on real-time feedback.
Machine learning-based planning algorithms can optimize the path planning and task sequencing for the robots, enabling them to perform tasks more efficiently and adapt to changing environments. By learning from past experiences and continuously improving their decision-making processes, the robots can enhance their overall performance and adaptability.
Furthermore, advanced sensing technologies, such as 3D cameras and LiDAR sensors, can provide the robots with detailed spatial information about their surroundings, enabling them to navigate complex construction environments more effectively. This enhanced perception capability can improve the robots' ability to avoid collisions, plan optimal paths, and interact with structural parts with greater precision.
Overall, the integration of these advanced technologies can enhance the system's overall performance, making it more robust, adaptable, and capable of handling a wider range of construction tasks with efficiency and accuracy.