Article:

Collaborative Robots, or Cobots, bring new possibilities for human-machineinteraction within the fabrication process, allowing each actor to contribute withtheir specific capabilities. However creative interaction brings unexpectedchanges, obstacles, complexities and non-linearities which are encountered inreal time and cannot be predicted in advance. This paper presents anexperimental methodology for robotic path planning using Machine Learning.The focus of this methodology is obstacle avoidance. A neural network isdeployed, providing a relationship between the robot's pose and its surroundings,thus allowing for motion planning and obstacle avoidance, directly integratedwithin the design environment. The method is demonstrated through a series ofcase-studies. The method combines haptic teaching with machine learning tocreate a task specific dataset, giving the robot the ability to adapt to obstacleswithout being explicitly programmed at every instruction. This opens the door toshifting to robotic applications for construction in unstructured environments,where adapting to the singularities of the workspace, its occupants and activitiespresents an important computational hurdle today.