Armin Sadeghi
Papers
12
Total Citations
116
H-Index
6
About
Armin Sadeghi’s research lies at the intersection of multi-robot systems, decentralized task allocation, and coverage control, with a strong emphasis on heterogeneous robot teams operating in dynamic and uncertain environments. His major contributions include developing a decentralized large neighborhood search algorithm for heterogeneous task allocation and sequencing, which enables robots to efficiently plan visits to diverse locations—a framework applicable to inspection and servicing missions. He has also advanced coverage control by designing methods for multiple event types, allowing heterogeneous robots to monitor varying environmental densities with provable guarantees, even in nonconvex spaces. Sadeghi’s work on learning submodular objectives for team orienteering addresses the challenge of unknown reward structures in environmental monitoring, while his minimum-time multi-robot planning ensures guarantees on total collected reward. With over 100 citations across his top papers, his impact is evident in both theoretical rigor and practical applicability. Notably, his research on regret-based Pareto front sampling introduces a novel approach to multi-objective robot planning, enabling error-bounded approximations of trade-offs. Sadeghi’s contributions are essential reading for researchers tackling real-world multi-robot coordination, from disaster response to persistent environmental surveillance.
Research Focus
Key Achievements
Top Papers
- 1
- 2Coverage Control for Multiple Event Types with Heterogeneous Robots25 citations · 2019
- 3Learning Submodular Objectives for Team Environmental Monitoring13 citations · 2021
- 4
- 5
- 6Optimizing Task Waiting Times in Dynamic Vehicle Routing6 citations · 2023
- 7
- 8
- 9Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems4 citations · 2022
- 10