5-Axis Robotic Motion Controller

The Five-Axis Robotic Motion Controller aims to bring physical input and output closer together through the design purpose-built tools for fabrication, which hopefully leads to many new creative opportunities for designers. Working from observations about the way architects design, this project explores the development of a novel 3D drawing tool or customized 5-axis digitizing arm that takes real-time input and translates movement patterns directly into machine code for robotic fabrication.

Direct manipulation of the robotic arm through the motion controller

The existing design-to-fabrication workflow


In the traditional robotic fabrication workflow, there is often a discrepancy between the original design intent and the final output, primarily because there is an intermediate step where the designer has to hand off a digital model to a fabrication consultant who has more intimate knowledge of the specific robotic CAM software and the fabrication process in general. Typically, this consultant will use programs such as Robot Studio or Master CAM to create the necessary tool paths for the design, however this process can often take a great deal of time. And, if during this process, modeling irregularities are found or fabrication problems arise due to reachability or collision detection issues, then the entire process must start anew.

A constant loop of error correction that starts back at the beginning until toolpaths are finally ready for production

Controller Concept

This project started very simply. I began by looking at the joint and axis configurations of the ABB-IRB 140 robot, one of the six axis robots available in the Harvard robotics lab. The design challenge then, was to design a tangible controller around these constraints. By using the same joint and axis configurations, the digitizing arm has a one to one relationship with the larger industrial robot.

Keeping it affordable

Outside of the development of a new robotic workflow, one of the primary goals of the project was to minimize costs. Given that all of the parts for this project were paid for out of pocket (a student's pocket), creating a low-cost solution was of utmost importance. But, beyond my own personal economic restrictions, I wanted this project to be seen as a do-it-yourself solution - something that could be built in any garage or workbench using easily purchased hardware parts and sensors and a few custom fabricated pieces. The entire controller was built for less than $200 dollars.

customized circuit board (on the left) which processes all of the sensor information and sends data to the interface. End effector (on right) with start and stop controls.
Each axis has a potentiometer which senses the new position of each arm
The input data is read by Firefly and processed through a few custom Grasshopper components
Grasshopper generates a forward kinematics digital representation of the real robot

The custom robotic simulation component written inside of Grasshopper outputs all of the necessary RAPID code to control the actual robot.  The method to hand the robot data is to stream the angle information from the digitizing arm directly to the robot through a network cable. In this method, a program is uploaded to the robot which tells it to sit and wait for any information being sent directly from the Grasshopper definition

Final motion controller with a direct-to-fabrication process

Although there has been considerable progress made in the digital tools used to control robots, I'd like to close by reiterating the fact that there is an identifiable problem in the existing design-to-fabrication process. I would like to propose an improved workflow for robotic fabrication. It is the hope of this project that the physical articulation of embodied input and output through purpose-built tools for fabrication can allow for wider adoption by and new creative opportunities for architects and designers. In turn, I hope this will help re-establish the relationship between designers and the physical fabrication process.


Acknowledgements

I would like to thank Harvard professors Martin Bechthold and Pangiotis Michalotos as well as Neil Gershenfeld from MIT's Center for Bits and Atoms for their support during the development of this project.


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