top of page

2 x 4 month Internships

Uber ATG

From September 2017 to December 2017 and May 2018 to August 2018, I worked at Uber ATG on their embedded systems team.

 

After parting ways with my former startup, I still had many thoughts as to how one would build a large engineering team to solve the self-driving car problem. One of my goals was to compare my own thoughts and understanding with an existing company to "close the loop" on my understanding of how to build a team of engineers in a robotics field. My internship at Uber was a great experience.

​

While there, I had the opportunity to be involved in a diverse range of projects related to their next generation self-driving vehicle system. Most of my work was confidential, but broadly I worked on the following projects.

​

  • Developed a system to control the rotation of a Lidar allowing for all sensors on all vehicles to be synchronized in their rotation. Built simulations to model the system and tune the controller as well as numerous tools developed in Python to interface with the sensor, auto characterize the system and validate controller performance.

  • Architected, integrated and benchmarked various power system configurations designed to supply power redundantly to the self-driving platform

  • Independently developed and facilitated FCC testing of the next generation telecommunication radio module for the self-driving platform

  • Developed many different breakout and interface boards for both my projects and existing projects to help facilitate production, testing and development 

Related Project Links

I was the Engineering Lead of the University of Waterloo's Formula Electric team (FSAE). I lead the engineering efforts of the 2018 vehicle, worked toward establishing a new team culture, and picked up on any project that needed extra hands.

I tested robot localization algorithms in ROS (robot operating system) including particle filter based localization using a Kalman filter on state change and LiDAR based scan matching using ICP for prediction and the ICP fitness score for resampling

Please reload

As part of a team of 4, we are developing an underwater vehicle to travers an obstacle course in the fastest time possible. My primary roll is to develop the GNC (guidance, navigation and controls) stack running on an onboard android phone

When learning more about modern control theory, I wanted to try a more challenging project that I had wanted to design for a while.  Using Simulink, I designed a simulation along with an LQR controller to balance a double pendulum even when subjected to noise. 

Please reload

bottom of page