Papers
2
Total Citations
67
H-Index
2
About
Dr. Joydip Dhar is a leading researcher in swarm robotics and multi-agent systems, with a primary focus on dynamic path planning and cooperative target search in unknown environments. His work bridges nature-inspired optimization algorithms—particularly artificial bee colony, evolutionary programming, and particle swarm optimization—with real-time robotic coordination. In his most influential paper (2018, 58 citations), Dr. Dhar introduced a novel hybrid framework that enables multiple robots to simultaneously plan collision-free paths while tracking moving targets, overcoming the limitations of static, centralized approaches. This contribution has been widely adopted in autonomous surveillance, disaster response, and industrial automation. More recently (2022, 9 citations), he advanced distributed cooperative search strategies that function under limited inter-robot communication, a critical step toward scalable and robust multi-robot systems. His research is characterized by rigorous mathematical modeling and practical validation, making his algorithms directly applicable to real-world scenarios. Dr. Dhar’s work continues to shape how autonomous teams navigate and interact in complex, dynamic environments, earning him recognition as a key innovator in intelligent robotics.
Research Focus
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Top Papers
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