Irianto Irianto
Papers
1
Total Citations
3
H-Index
1
About
Irianto Irianto’s research focuses on the intersection of robotics, swarm intelligence, and sensor-based navigation, with a particular emphasis on odor sensing for autonomous movement. His most-cited work, “Particle Swarm Optimization (PSO) for Simulating Robot Movement on Two-Dimensional Space Based on Odor Sensing” (2017, 3 citations), introduces a novel application of PSO algorithms to guide robots in tracking odor sources across two-dimensional environments. This contribution bridges computational optimization and bio-inspired robotics, offering a framework for robots to navigate complex, dynamic spaces using olfactory cues. By simulating how particles in a swarm converge toward a target, Irianto’s approach provides a cost-effective, scalable method for tasks like environmental monitoring, search-and-rescue, and hazardous material detection. While his citation count reflects a niche but growing field, his work represents an early step in integrating swarm intelligence with chemical sensing—a domain with expanding relevance in autonomous systems. Irianto’s research underscores the potential of combining nature-inspired algorithms with robotic perception, laying groundwork for future innovations in odor-guided navigation and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1