Papers
14
Total Citations
139
H-Index
6
About
Bahar Haghighat is a leading researcher in swarm robotics and programmable self-assembly, drawing inspiration from biological systems like army ants to create adaptive, decentralized robotic collectives. Her major contributions include the design of "Eciton robotica," an adaptive self-assembling soft robot collective that mimics the bridge-building behavior of Eciton army ants, enabling dynamic structure formation without centralized control. She also developed "Lily," a miniature floating robotic platform for fluid-mediated stochastic self-assembly, advancing the construction of target structures at centimetric and sub-millimetric scales. Her work on modeling and controlling stochastic self-assembly processes has been foundational, with her top-cited paper (34 citations) and subsequent studies on ruleset synthesis for programmable self-assembly (11-14 citations) demonstrating significant impact. Haghighat has also applied particle swarm optimization to multi-robot tasks, such as target search and spacecraft hull inspection, achieving up to 6 citations per paper. Her recent focus on collective Bayesian decision-making for surface inspection with miniaturized robots showcases her ongoing innovation in swarm intelligence. With a portfolio of highly cited papers and a knack for translating biological principles into robotic systems, Haghighat is a pivotal figure in the field, inspiring students and researchers to explore the frontiers of decentralized robotics and self-assembly.
Research Focus
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