Details
| Original language | English |
|---|---|
| Pages (from-to) | 108-113 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 106 |
| Early online date | 10 Mar 2022 |
| Publication status | Published - 2022 |
| Event | 9th CIRP Conference on Assembly Technology and Systems, CATS 2022 - KU Leuven, Belgium Duration: 6 Apr 2022 → 8 Apr 2022 |
Abstract
In the construction industry, as well as during the assembly of large-scale components, the required workspaces usually cannot be served by a stationary robot. Instead, mobile robots are used to increase the accessible space. Here, the problem arises that the accuracy of such systems is not sufficient to meet the tolerance requirements of the components to be produced. Furthermore, there is an additional difficulty in the trajectory planning process since the exact dimensions of the pre-manufactured parts are unknown. Hence, existing static planning methods cannot be exerted on every application. Recent approaches present dynamic planning algorithms based on specific component characteristics. For example, the latest methods follow the contour by a force-controlled motion or detect features with a camera. However, in several applications such as welding or additive manufacturing in construction, no contact force is generated that could be controlled. Vision-based approaches are generally restricted by varying materials and lighting conditions, often found in large-scale construction. For these reasons, we propose a more robust approach without measuring contact forces, which, for example, applies to large-scale additive manufacturing. We based our algorithm on a high-precision 2D line laser, capable of detecting different feature contours regardless of material or lightning. The laser is mounted to the robot's end-effector and provides a depth profile of the component's surface. From this depth data, we determine the target contour and control the manipulator to follow it. Simultaneously we vary the robot's speed to adjust the feed rate depending on the contour's shape, maintaining a constant material application rate. As a proof of concept, we apply the algorithm to the additive manufacturing of two-layer linear structures made from spray PU foam. When making these structures, each layer must be positioned precisely on the previous layer to obtain a straight wall and prevent elastic buckling or plastic collapse. Initial experiments show improved layer alignment within 10 % of the layer width, as well as better layer height consistency and process reliability.
Keywords
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ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 106, 2022, p. 108-113.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Contour Tracking Control for Mobile Robots applicable to Large-scale Assembly and Additive Manufacturing in Construction
AU - Lachmayer, Lukas
AU - Recker, Tobias
AU - Raatz, Annika
N1 - Funding Information: The authors gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG – German Research Foundation) – Project no. 414265976. The authors would like to thank the DFG for the support within the SFB/Transregio 277 – Additive manufacturing in construction. (Subproject B04)
PY - 2022
Y1 - 2022
N2 - In the construction industry, as well as during the assembly of large-scale components, the required workspaces usually cannot be served by a stationary robot. Instead, mobile robots are used to increase the accessible space. Here, the problem arises that the accuracy of such systems is not sufficient to meet the tolerance requirements of the components to be produced. Furthermore, there is an additional difficulty in the trajectory planning process since the exact dimensions of the pre-manufactured parts are unknown. Hence, existing static planning methods cannot be exerted on every application. Recent approaches present dynamic planning algorithms based on specific component characteristics. For example, the latest methods follow the contour by a force-controlled motion or detect features with a camera. However, in several applications such as welding or additive manufacturing in construction, no contact force is generated that could be controlled. Vision-based approaches are generally restricted by varying materials and lighting conditions, often found in large-scale construction. For these reasons, we propose a more robust approach without measuring contact forces, which, for example, applies to large-scale additive manufacturing. We based our algorithm on a high-precision 2D line laser, capable of detecting different feature contours regardless of material or lightning. The laser is mounted to the robot's end-effector and provides a depth profile of the component's surface. From this depth data, we determine the target contour and control the manipulator to follow it. Simultaneously we vary the robot's speed to adjust the feed rate depending on the contour's shape, maintaining a constant material application rate. As a proof of concept, we apply the algorithm to the additive manufacturing of two-layer linear structures made from spray PU foam. When making these structures, each layer must be positioned precisely on the previous layer to obtain a straight wall and prevent elastic buckling or plastic collapse. Initial experiments show improved layer alignment within 10 % of the layer width, as well as better layer height consistency and process reliability.
AB - In the construction industry, as well as during the assembly of large-scale components, the required workspaces usually cannot be served by a stationary robot. Instead, mobile robots are used to increase the accessible space. Here, the problem arises that the accuracy of such systems is not sufficient to meet the tolerance requirements of the components to be produced. Furthermore, there is an additional difficulty in the trajectory planning process since the exact dimensions of the pre-manufactured parts are unknown. Hence, existing static planning methods cannot be exerted on every application. Recent approaches present dynamic planning algorithms based on specific component characteristics. For example, the latest methods follow the contour by a force-controlled motion or detect features with a camera. However, in several applications such as welding or additive manufacturing in construction, no contact force is generated that could be controlled. Vision-based approaches are generally restricted by varying materials and lighting conditions, often found in large-scale construction. For these reasons, we propose a more robust approach without measuring contact forces, which, for example, applies to large-scale additive manufacturing. We based our algorithm on a high-precision 2D line laser, capable of detecting different feature contours regardless of material or lightning. The laser is mounted to the robot's end-effector and provides a depth profile of the component's surface. From this depth data, we determine the target contour and control the manipulator to follow it. Simultaneously we vary the robot's speed to adjust the feed rate depending on the contour's shape, maintaining a constant material application rate. As a proof of concept, we apply the algorithm to the additive manufacturing of two-layer linear structures made from spray PU foam. When making these structures, each layer must be positioned precisely on the previous layer to obtain a straight wall and prevent elastic buckling or plastic collapse. Initial experiments show improved layer alignment within 10 % of the layer width, as well as better layer height consistency and process reliability.
KW - separated by semicolons
KW - Type your keywords here
UR - http://www.scopus.com/inward/record.url?scp=85127468945&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2022.02.163
DO - 10.1016/j.procir.2022.02.163
M3 - Conference article
AN - SCOPUS:85127468945
VL - 106
SP - 108
EP - 113
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 9th CIRP Conference on Assembly Technology and Systems, CATS 2022
Y2 - 6 April 2022 through 8 April 2022
ER -