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About

Sazalinsyah Razali is a robotics and artificial intelligence researcher whose work centers on multi-robot systems, bio-inspired algorithms, and autonomous cooperative behavior. He is best known for pioneering the application of biological immune system principles to multi-robot coordination, particularly the challenging shepherding problem — where multiple robots must collaboratively herd a group of agents toward a goal. His foundational contributions, including "Multi-robot cooperation using immune network with memory" (2009) and subsequent refined models, draw on immune network theories and somatic hypermutation to design sophisticated cooperative mechanisms that mirror how living organisms respond to external threats. These works have collectively garnered over 30 citations, establishing him as a notable voice in biologically inspired robotics research. Beyond algorithmic innovation, Razali has demonstrated a commitment to accessibility in robotics education, authoring an overview of simulation tools tailored for non-expert developers, helping lower the barrier to entry in multi-robot experimentation. His more recent work on multi-agent pathfinding using improved flood fill algorithms reflects a continued evolution toward practical autonomous navigation solutions. Razali's research bridges theoretical biology and applied robotics in ways that meaningfully advance how autonomous systems learn, adapt, and cooperate.