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About

Tushar Kusnur is a robotics researcher whose work spans autonomous navigation, simultaneous localization and mapping (SLAM), and multi-robot planning systems. His most recognized contribution, "Virtual Occupancy Grid Map for Submap-based Pose Graph SLAM and Planning in 3D Environments" (2018, 46 citations), introduced an innovative mapping framework that enables robots to correct accumulated drift through loop closures while preserving free-space information — a significant advance for 3D autonomous navigation. This work demonstrated his ability to bridge theoretical elegance with practical system design. Kusnur's research has increasingly focused on the challenges of coordinating fleets of unmanned aerial vehicles (UAVs), with his multi-UAV coverage planning framework addressing persistent coverage — environments where coverage quality degrades over time — alongside global collision deconfliction. His later work on decomposition-free coverage path planning pushes toward more general, flexible solutions that move beyond the constraints of traditional hierarchical approaches. Across his body of work, Kusnur tackles problems at the intersection of mapping, planning, and multi-agent coordination, contributing tools that make autonomous robotic systems more robust and scalable in complex real-world environments. His research is particularly relevant for applications in search and rescue, environmental monitoring, and autonomous inspection.