Daniel Iercan
Papers
6
Total Citations
135
H-Index
4
About
Daniel Iercan is a robotics researcher whose work focuses on optimal path planning for mobile robots operating in complex, obstacle-rich environments. His primary research areas include bio-inspired optimization algorithms, multi-robot coordination, and autonomous navigation. Iercan’s most significant contribution is the development and hybridization of Gravitational Search Algorithms (GSA) and Particle Swarm Optimization (PSO) for robot path planning, with his seminal 2013 paper on GSA-based path planning accumulating 72 citations. He pioneered a hybrid PSO-GSA approach that balances multiple objectives—such as path length, safety, and energy efficiency—in static environments with danger zones, a work cited 29 times. Iercan also extended these algorithms to multi-robot systems, enabling coordinated navigation for holonomic wheeled platforms. Beyond optimization, he created the nRobotic platform, a software framework for managing heterogeneous robots with different capabilities and hardware, facilitating mission planning and inter-robot communication. His research demonstrates practical applications, including indoor navigation using traffic signs and sensor fusion from sonar, infrared, and cameras. Iercan’s work bridges theoretical optimization with real-world robotic systems, offering scalable solutions for autonomous missions in unknown environments.
Research Focus
Key Achievements
Top Papers
- 1Optimal Robot Path Planning Using Gravitational Search Algorithm72 citations · 2013
- 2
- 3Multi-robot GSA- and PSO-based optimal path planning in static environments21 citations · 2013
- 4nRobotic: A platform for managing robots with different capabilities5 citations · 2009
- 5nRobotic applications to path planning for mobile robots in missions4 citations · 2012
- 6