Batsaikhan Dugarjav

About

Batsaikhan Dugarjav is a leading researcher in autonomous robotics, specializing in coverage path planning (CPP) for mobile robots operating in unknown environments. His work focuses on developing algorithms that enable single and multi-robot systems to achieve complete, time-efficient coverage—critical for applications like floor cleaning, industrial inspection, and search-and-rescue. Dugarjav’s major contributions include pioneering scan matching online cell decomposition (46 citations), which allows robots to adaptively map and cover unknown spaces without prior knowledge, and flow network-based multi-robot CPP (38 citations), optimizing task allocation for efficiency. He also advanced sensor-based incremental Boustrophedon decomposition (16 citations) and adaptive online cell decomposition for non-rectilinear environments (9 citations), addressing real-world complexities. His foundational 2011 paper on multi-robot complete coverage path planning (28 citations) remains a key reference in the field. Dugarjav’s work has garnered over 137 citations, reflecting its impact on both theoretical robotics and practical autonomous systems. His algorithms are particularly noted for balancing completeness with computational efficiency, making them suitable for real-time deployment in dynamic settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Scan matching online cell decomposition for coverage path planning in an unknown environment

   46 citations · 2013
2. 2
   Time-efficient and complete coverage path planning based on flow networks for multi-robots

   38 citations · 2013
3. 3
   Complete coverage path planning for multi-robots based on

   28 citations · 2011
4. 4
   Sensor-Based Incremental Boustrophedon Decomposition for Coverage Path Planning of a Mobile Robot

   16 citations · 2012
5. 5
   Adaptive online cell decomposition with a laser range finder in unknown non-rectilinear environments

   9 citations · 2017

Key Collaborators

Contact & Links