Curriculum Vitae
General Information
Full Name | Justin M. Kottinger |
Date of Birth | 23rd October 1997 |
Languages | English |
Education
- 2024
Ph.D in Robotics
University of Colorado Boulder, CO
- Department of Robotics
- 2024
M.S in Aerospace Engineering
University of Colorado Boulder, CO
- Concentration in Autonomous Systems / Robotics
- 2019
B.A in Physics
Whittier College, Whittier, CA
- Minor in Mathematics
- Member of Sigma Pi Sigma and Pi Mu Epsilon
Skills
-
Programming Languages
- C++
- Python
- MATLAB
- Julia
-
Modeling and Simulation
- ROS / ROS 2
- Gazebo
- Simulink
-
Operating Systems
- Mac OS
- Linux
- Windows
-
Workflow
- Git
- GitHub
- Bitbucket
- Jira
Relevant Coursework
-
Core Courses
- Linear Control Theory
- Mathematical Statistics
- Statistical Estimation
- Decision Making Under Uncertainty
- Spacecraft Attitude, Dynamics, and Control
-
Research Specific Courses
- Algorithmic Motion Planning
- Statistical Learning
- Verifcation and Synthesis of Stochastic Systems
- Coordination and Control of Multi-Agent Systems
- Hybrid Control Systems
Experience
- 2023-present
Robotic Navigation Software Engineer Graduate Intern
Trimble Corporation, Westminster, Colorado, USA
- Conceptualized and developed a containerized physics-based simulator using Docker, ROS2, and Gazebo to test GNC service product.
- Implemented motion planning algorithms for line acquisition of autonomous agricultural vehicles.
- Enabled autonomous line acquisition in reverse and validated on-vehicle.
- 2022-2023
Fault Identification of Autonomous Systems via Bayesian Inference
University of Colorado Boulder, Colorado, USA
- Utilized Bayesian hypothesis testing to accurately identify faults and unknown anomalies within autonomous robots.
- Implemented statistical estimation algorithms such as Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter in C++.
- Conceptualized and implemented moving time-windows and fault-partitioning to improve the baseline approach by 20%.
- Delivered a final product that identified faults and unknown anomalies with over 95% success.
- 2021-2022
Autonomous Systems Software Engineer Graduate Intern
The Aerospace Corporation, El Segundo, California, USA
- Assisted in design, control, and system identification of an omni-directional octocopter.
- Implemented advanced algorithms and data structures to create novel flight software in Python and C++.
- Validated custom flight modules inside simulation using ROS and Gazebo, and tested them onboard the vehicle.
- Assisted in the development of a Risk-Aware framework for Uber ATG's self-driving vehicle stack.
- Formulated probabilistic dynamics propagation and intent models for self-driving vehicle and pedestrian models.
- Implemented probabilistic models in Python and C++ using Bayesian derived statistics.
Presentations
- 2022
- Conflict‐based Search for Multi‐Robot Motion Planning with Kinodynamic Constraints
- International Conference On Intelligent Robots and Systems (IROS)
- 2022
- Explainable Multi-Robot Motion Planning
- Robotics Summer Seminar
- 2022
- Conflict‐Based Search for Explainable Multi‐Agent Path Finding
- International Conference on Automated Planning and Scheduling (ICAPS)
- 2021
- MAPS‐X -- Explainable Multi‐Robot Motion Planning via Segmentation
- International Conference on Robotics and Automation (ICRA)
Achievements
- 2021
- Recipient, Graduate Assistantships for Areas of National Need (GAANN) Fellowship
- 2019
- Recipient, Undergraduate Award for Outstanding Academic Performance in the Major
- Recipient, Magna Cum Laude Honors
- 2018
- 2nd Place, Research Presentation Competition at NSF funded REU
- Captain, Whittier College NCAA Baseball Team
Open Source Projects
- 2023-now
Multi-Robot OMPL
- An extended version of the reverered Open Motion Planning Library (OMPL) designed for decoupled multi-robot motion planning.
- 2022-now
Kinodynamic Conflict-Based Search (K-CBS)
- A scalable and generalizable decoupled multi-robot motion planning algorithm that provides completeness guarantees.