Robotic Feedback Loops

Implementing two-way communication in architecturally focused robotic pick and place operations.

The use of robots in the fabrication of complex architectural structures is increasing in popularity. However, architectural robotic workflows still require convoluted and time-consuming programming in order to execute complex fabrication tasks. Additionally, an inability for robots to adapt to different environments further highlights concerns around the robotic manipulator as a primary construction tool. There are four key issues currently present in robotic fabrication for architectural applications. Firstly, an inability to adapt to unknown environments; Secondly, a lack of autonomous decision making; Thirdly, an inability to locate, recognise, and then manipulate objects in the operating environment; Fourthly a lack of error detection if a motion instruction conflicts with environmental constraints. This project begins to resolve these critical issues by seeking to integrate a feedback loop in a robotic system to improve perception, interaction and manipulation of objects in a robotic working environment. The implementation of a robotic feedback loop in this way demonstrates both the future potential and success of robots in construction applications. The research begins to develop pathways through which to integrate new types of technologies such as machine learning and deep learning in order to improve the accuracy, speed and reliability of perception-controlled robotic systems through learned behaviours.