Justin M. Kottinger

Hello, I’m Justin Kottinger, an ambitious Ph.D. Candidate in the Department of Robotics at the University of Colorado Boulder, specializing in autonomous robotic motion and task planning. Anticipating the completion of my Ph.D. in Robotics by the summer of 2024, my academic journey includes a B.A. in Physics and Minor in Mathematics from Whittier College, complemented by an M.S. in Aerospace Engineering with a focus in Autonomous Systems from the University of Colorado Boulder.

My expertise encompasses motion planning and task planning for both single and multi-robot teams, a proficiency refined through courses like “Algorithmic Motion Planning,” “Decision Making Under Uncertainty,” and “Verification and Synthesis of Stochastic Systems.” This expertise is not just theoretical; I’ve contributed significantly to the field through several research publications and active involvement in the development of the “Open Motion Planning Library,” a popular C++ framework for motion planning utilized in both industry and research tasks.

In the professional domain, my skills extend beyond academic research. I am well-versed in continuous integration and continuous development (CI/CD) tools, utilizing Jira and Git for efficient project management. I have successfully implemented code for safe autonomous flight on an omni-directional drone while working as an Auontomous Systems Engineer Intern at the Aerospace Corporation. I also work part-time as a robotic navigation software engineer for Trimble Corporation’s autonomous agriculture equipment development team where I have enhanced the line-acquisition software for autonomous agricultural equipment.

Having actively engaged in projects that bridge the gap between academia and industry, I am eager to apply my comprehensive skill set to industry challenges, contributing to the advancement of autonomous robotics. Join me on this journey as we shape the future of autonomous systems together!

News

Jan 1, 2024	My website is live!
Dec 1, 2023	I have successfuly completed my comprehensive exam to become a Ph.D. candidate.
Nov 25, 2023	Our manuscript on Uncertain Multi-Robot Motion Planning has been accepted into IEEE RAL

Notable Publications

Chance-Constrained Multi-Robot Motion Planning Under Gaussian Uncertainties

Anne Theurkauf, Justin Kottinger, Nisar Ahmed, and 1 more author

IEEE Robotics and Automation Letters, 2023

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We consider a chance-constrained multi-robot motion planning problem in the presence of Gaussian motion and sensor noise. Our proposed algorithm, CC-K-CBS, leverages the scalability of kinodynamic conflict-based search (K-CBS) in conjunction with the efficiency of Gaussian belief trees as used in the Belief- A framework, and inherits the completeness guarantees of Belief- A ’s low-level sampling-based planner. We also develop three different methods for robot-robot probabilistic collision checking, which trade off computation with accuracy. Our algorithm generates motion plans driving each robot from its initial to goal state while accounting for uncertainty evolution with chance-constrained safety guarantees. Benchmarks compare computation time to conservatism of the collision checkers, in addition to characterizing the performance of the planner as a whole. Results show that CC-K-CBS scales up to 30 robots.
Conflict-Based Search for Multi-Robot Motion Planning with Kinodynamic Constraints

Justin Kottinger, Shaull Almagor, and Morteza Lahijanian

Oct 2022

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Multi-robot motion planning (MRMP) is the fundamental problem of finding non-colliding trajectories for multiple robots acting in an environment, under kinodynamic constraints. Due to its complexity, existing algorithms are either incomplete, or utilize simplifying assumptions. This work introduces Kinodynamic Conflict-Based Search (K-CBS), a decentralized MRMP algorithm that is general, scalable, and probabilistically complete. The algorithm takes inspiration from successful solutions to the discrete analogue of MRMP over finite graphs, known as Multi-Agent Path Finding (MAPF). Specifically, we adapt ideas from Conflict-Based Search (CBS)-a popular decentralized MAPF algorithm-to the MRMP setting. The novelty of our approach is that we work directly in the continuous domain, without discretization. In particular, the kinodynamic constraints are treated natively. K-CBS plans for each robot individually using a low-level planner and grows a conflict tree to resolve collisions between robots by defining constraints. The low-level planner can be any sampling-based, tree-search algorithm for kinodynamic robots, thus lifting existing planners for single robots to the multi-robot setting. We show that K-CBS inherits the (probabilistic) completeness of the low-level planner. We illustrate the generality and performance of K-CBS in several case studies and benchmarks.