Selected research
Decentralized learning MPC
A safe-set learning model predictive control framework with APF warm starts, learned terminal sets, and priority-aware spatial constraints for coordinated agents.
Decentralized agents need to improve repeated motion tasks while preserving separation, obstacle constraints, and a usable route to the goal. Learning from earlier trajectories is only useful when unsafe or incomplete runs are kept out of the learned safe set.
The implementation combines artificial-potential-field initialization, decentralized CasADi/IPOPT learning MPC, sampled terminal safe sets, time-to-go costs, and priority-aware spatial hyperplanes.
The broader public testbed also separates heterogeneous ground, surface, and underwater dynamics from guidance, estimation, optimization, and reporting modules.
The repository provides a configurable computational research testbed and curated simulation artifacts for repeated multi-agent studies.
The project is ongoing research software; it is not presented as a peer-reviewed result or as hardware validation.
Citation
Suggested software citation · public research codebase