Justin Kottinger
Papers
2
Total Citations
51
H-Index
2
About
Justin Kottinger is a leading researcher in multi-robot motion planning (MRMP), specializing in algorithms that enable teams of robots to navigate complex environments safely and efficiently. His most influential work, "Conflict-Based Search for Multi-Robot Motion Planning with Kinodynamic Constraints" (2022, 46 citations), tackles the fundamental challenge of coordinating multiple robots under real-world physical constraints like velocity and acceleration limits. This paper introduced a scalable, complete approach to a problem that previously required simplifying assumptions, establishing a new benchmark in the field. Kottinger further advanced the state of the art with "Chance-Constrained Multi-Robot Motion Planning Under Gaussian Uncertainties" (2023, 5 citations), where he addressed the critical issue of real-world noise in robot motion and sensors. By integrating Gaussian belief trees with kinodynamic conflict-based search, his CC-K-CBS algorithm provides robust, probabilistic safety guarantees—a vital step toward deploying multi-robot systems in unpredictable environments. Kottinger’s work bridges the gap between theoretical planning and practical deployment, making him a key figure in the next generation of autonomous multi-robot coordination.
Research Focus
Key Achievements
Top Papers
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