Masoud Dadgar

Papers

Total Citations

177

H-Index

About

Masoud Dadgar is a leading researcher in multi-robot systems and swarm intelligence, with a primary focus on cooperative target searching in unknown environments. His most impactful contribution is the development of a Particle Swarm Optimization (PSO)-based multi-robot cooperation method, published in 2015, which has garnered 148 citations and remains a foundational reference in the field. Dadgar’s work innovatively applies bio-inspired algorithms to robotics, treating robots as ions to model collective behavior. In his 2017 study, he introduced a repulsion mechanism between similar ions to enhance diversity and convergence speed in RDPSO (Robotic Dynamic Particle Swarm Optimization), a concept he further refined in his 2019 paper on Repulsion-Based RDPSO (RbRDPSO), which earned 22 citations. These contributions address critical challenges in multi-robot coordination, such as balancing exploration and exploitation to improve search efficiency. Dadgar’s research is notable for its practical impact on autonomous systems, offering scalable solutions for real-world applications like disaster response and environmental monitoring. His work continues to inspire advances in swarm robotics, making him a key figure in the intersection of optimization algorithms and multi-agent systems.