Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
Abioye, Ayodeji O., Deshmukh, Jayati, Georgara, Athina, Price, Dominic, Nguyen, Tuyen, Landowska, Aleksandra, Bennaceur, Amel, Fischer, Joel E., Ramchurn, Sarvapali D.
–arXiv.org Artificial Intelligence
This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.
arXiv.org Artificial Intelligence
Jul-4-2025
- Country:
- North America > United States
- Texas > Travis County
- Austin (0.04)
- New York > New York County
- New York City (0.05)
- Nevada > Clark County
- Las Vegas (0.04)
- Texas > Travis County
- Europe
- Czechia > Prague (0.04)
- United Kingdom > England
- Nottinghamshire > Nottingham (0.16)
- Hampshire > Southampton (0.06)
- Tyne and Wear > Newcastle (0.05)
- Buckinghamshire > Milton Keynes (0.05)
- Germany > Baden-Württemberg
- Karlsruhe Region > Karlsruhe (0.04)
- Asia
- Taiwan > Taiwan Province
- Taipei (0.04)
- Malaysia > Kuala Lumpur
- Kuala Lumpur (0.04)
- Japan > Honshū
- Kantō > Kanagawa Prefecture > Yokohama (0.05)
- China > Shaanxi Province
- Xi'an (0.05)
- Taiwan > Taiwan Province
- North America > United States
- Genre:
- Research Report (0.40)
- Technology: