Papers

30

Total Citations

297

H-Index

10

About

Sedat Dogru is a leading researcher in energy-efficient mobile robotics, specializing in coverage path planning (CPP) and power modeling for autonomous field robots. His work addresses critical challenges in robotics—namely, how to optimize energy consumption during complex tasks such as autonomous demining, farming, and disinfection, particularly on non-flat, three-dimensional terrain. Dogru’s major contributions include pioneering physics-based power models for skid-steered and differential drive robots, enabling accurate energy estimation along straight and rotational trajectories. His highly cited paper, “Energy Efficient Coverage Path Planning for Autonomous Mobile Robots on 3D Terrain” (38 citations), broke new ground by incorporating terrain relief into CPP, moving beyond traditional flat-environment approaches. Another influential work, “A Physics-Based Power Model for Skid-Steered Wheeled Mobile Robots” (37 citations), provides a foundational framework for energy-optimal mission planning. Dogru also developed ECO-CPP, an energy-constrained online coverage path planning algorithm, and extended his models to headland turn optimization and payload-carrying robots. His recent research on UV-C disinfection robots demonstrates the practical impact of his path and trajectory planning expertise. With over 200 citations across his top publications, Dogru’s work is essential reading for anyone interested in sustainable, autonomous field robotics.

Research Focus

Key Achievements

10
H-Index
30
Papers
297
Total Citations
10
Avg Citations/Paper
🏆 Most Cited Paper
Energy Efficient Coverage Path Planning for Autonomous Mobile Robots on 3D Terrain
38 citations · 2015
📈 Most Prolific Year: 2018 (5 Papers)
🤝 Key Collaborators: 18
🏛 Institutions: University of Coimbra, Institute for Systems Engineering and Computers

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago