Abstract
Collaborative Robots, or Cobots, bring new possibilities for human-machine
interaction within the fabrication process, allowing each actor to contribute with their specific capabilities. However creative interaction brings unexpected
changes, obstacles, complexities and non-linearities which are encountered in
real time and cannot be predicted in advance. This paper presents an
experimental methodology for robotic path planning using Machine Learning.
The focus of this methodology is obstacle avoidance. A neural network is
deployed, providing a relationship between the robot's pose and its surroundings, thus allowing for motion planning and obstacle avoidance, directly integrated within the design environment. The method is demonstrated through a series of case-studies. The method combines haptic teaching with machine learning to create a task specific dataset, giving the robot the ability to adapt to obstacles without being explicitly programmed at every instruction. This opens the door to shifting to robotic applications for construction in unstructured environments, where adapting to the singularities of the workspace, its occupants and activities presents an important computational hurdle today.
interaction within the fabrication process, allowing each actor to contribute with their specific capabilities. However creative interaction brings unexpected
changes, obstacles, complexities and non-linearities which are encountered in
real time and cannot be predicted in advance. This paper presents an
experimental methodology for robotic path planning using Machine Learning.
The focus of this methodology is obstacle avoidance. A neural network is
deployed, providing a relationship between the robot's pose and its surroundings, thus allowing for motion planning and obstacle avoidance, directly integrated within the design environment. The method is demonstrated through a series of case-studies. The method combines haptic teaching with machine learning to create a task specific dataset, giving the robot the ability to adapt to obstacles without being explicitly programmed at every instruction. This opens the door to shifting to robotic applications for construction in unstructured environments, where adapting to the singularities of the workspace, its occupants and activities presents an important computational hurdle today.
Originalsprog | Engelsk |
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Titel | Architecture in the Age of the 4th Industrial Revolution : Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019 |
Redaktører | José Pedro Sousa, Joäo Pedro Xavier, Goncalo Castro Henriques |
Antal sider | 10 |
Udgivelsessted | Porto |
Publikationsdato | 2019 |
Sider | 201-210 |
Artikelnummer | 280 |
Status | Udgivet - 2019 |
Begivenhed | eCAADe: Architecture in the Age of the 4th Industrial Revolution - University of Porto (FAUP), Faculty of Architecture, Porto, Portugal Varighed: 11 sep. 2019 → 13 sep. 2019 https://ecaadesigradi2019.arq.up.pt/ |
Konference
Konference | eCAADe |
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Lokation | University of Porto (FAUP), Faculty of Architecture |
Land/Område | Portugal |
By | Porto |
Periode | 11/09/2019 → 13/09/2019 |
Internetadresse |
Navn | ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution |
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Kunstnerisk udviklingsvirksomhed (KUV)
- Nej