Learning-Based Distributed Aerial Shepherding of UGV Swarms
Jun 1, 2025·
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0 min read
Reda El Marhouch
Btissam El Khamlichi
Amal El Fallah Seghrouchni
Abstract
A UAV acts as an aerial shepherd guiding a ground vehicle that leads a UGV swarm, while the UGVs autonomously select local leaders to improve cohesion and reduce communication overhead. An embedding-based observation-history mechanism lets agents process the information needed for the shepherding task.
Type
Publication
21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), 93-98
Status
Peer-reviewed
Authors
Ph.D. Student in Artificial Intelligence
I am a Ph.D. student in Artificial Intelligence at Ai Movement, the International Artificial Intelligence Center of Morocco at UM6P. My research interests are multi-agent reinforcement learning, graph-based communication between agents, and heterogeneous swarm coordination — in particular how structural priors over an agent’s communication graph can make coordination scale under partial observability.
Before starting the Ph.D. I graduated from the School of Information Sciences (ESI) as a Data and Knowledge engineer, and worked on applied computer vision and conversational AI.