Marijan Vukosavljev

Papers

Total Citations

268

H-Index

About

Marijan Vukosavljev develops foundational algorithms for safe, real-time multi-robot motion planning. His most impactful work introduces a distributed model predictive control (DMPC) framework that enables multiple robots to generate non-colliding trajectories online, using an event-triggered, on-demand collision avoidance strategy—a paper that has garnered 245 citations for its practical, real-time approach to coordination. Complementing this, Vukosavljev has advanced modular motion planning through a framework that leverages safe-by-design motion primitives, constraining allowable sequences to guarantee safety in gridded workspaces. His research also addresses robust controller synthesis, formulating piecewise affine feedback laws as reach control problems to ensure safe robot maneuvers under high-level specifications. By bridging theoretical control with deployable, computationally efficient solutions, Vukosavljev’s work directly supports the reliable operation of autonomous robot teams in dynamic environments. His contributions are essential reading for researchers working at the intersection of distributed control, formal methods, and multi-agent systems.