Adittyo Paul

Papers

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About

Adittyo Paul is a researcher focused on advancing the field of multi-agent path finding (MAPF), with a particular emphasis on real-time coordination and plan execution for multi-robot systems. His major contributions center on developing efficient rescheduling algorithms that address the critical challenge of agent delays during execution. Instead of costly full re-planning, Paul’s work introduces innovative methods to dynamically reorder the sequence in which agents pass through shared locations, enabling rapid, near-optimal adjustments. His most-cited paper, "A Fast Rescheduling Algorithm for Real-Time Multi-Robot Coordination" (2023), has garnered 3 citations, while two subsequent 2024 papers on the same topic each hold 2 citations. Though early in his career, Paul’s research directly tackles a practical bottleneck in autonomous systems—ensuring robustness and efficiency when plans go awry. His work holds promise for applications in warehouse logistics, autonomous vehicle fleets, and other domains requiring reliable multi-agent coordination. By focusing on lightweight, real-time solutions, Paul is carving a niche that bridges theoretical MAPF algorithms and real-world deployment constraints.