Julian Morelli
Papers
2
Total Citations
29
H-Index
2
About
Julian Morelli is a leading researcher in the field of distributed robotic systems, with a core focus on scalable perception and autonomous decision-making for large-scale environmental monitoring. His work primarily addresses the challenges of gas sensing, mapping, and path planning for Very Large-Scale Robotic (VLSR) systems. Morelli’s major contribution lies in developing decentralized approaches that allow swarms of robots to collaboratively build probabilistic representations of their environment. He pioneered the use of Hilbert maps for this purpose, a technique that elegantly formulates the mapping problem as a multi-class classification task, enabling efficient and continuous obstacle and gas distribution mapping without centralized data fusion. His most-cited work, "Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps" (21 citations), demonstrates how these maps can be integrated with information-driven path planning to guide robots toward the most informative sampling locations. This foundational work, alongside his related paper on integrated mapping for VLSR systems, establishes Morelli as a key innovator in creating the algorithmic backbone for next-generation, autonomous environmental sensing networks.
Research Focus
Key Achievements
Top Papers
- 1
- 2