Asan Adamanov

Papers

Total Citations

H-Index

About

Asan Adamanov is a robotics researcher specializing in autonomous navigation and motion planning, with a particular focus on real-time obstacle avoidance in industrial environments. His most-cited work, "Comparative Analysis of Local Trajectory Planning Algorithms in ROS2" (2025, 3 citations), provides a critical evaluation of three key local path-planning algorithms—Dynamic Window Approach (DWB), Model Predictive Path Integral (MPPI), and Regulated Pure Pursuit (RPP)—within the ROS 2 framework. This study offers valuable insights for practitioners seeking robust solutions for unpredictable settings, highlighting trade-offs in computational efficiency, safety, and path smoothness. Adamanov’s contributions help bridge the gap between theoretical planning algorithms and practical deployment in dynamic workspaces. Though early in his career, his work has already informed the design of safer, more responsive robotic systems. By systematically benchmarking these planners, he has laid groundwork for future advances in adaptive navigation. His research is particularly relevant for students and engineers working on autonomous ground vehicles, warehouse robots, and collaborative industrial manipulators, where reliable local planning is critical for real-world operation.