Roham Shakiba

Papers

Total Citations

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About

Roham Shakiba is a robotics researcher whose work centers on intelligent path planning for humanoid robots, with a particular focus on soccer-playing applications. His major contributions lie in the development of optimization-driven navigation algorithms that enable humanoid robots to move efficiently and safely in dynamic, obstacle-filled environments. Shakiba’s most cited work, “An improved PSO-based path planning algorithm for humanoid soccer playing robots” (2013, 15 citations), introduces a novel approach that combines Ferguson splines with Particle Swarm Optimization (PSO) to generate smooth, collision-free paths. This algorithm iteratively refines random spline parameters to produce optimal trajectories toward the ball, balancing path length and computational efficiency. Building on this, his 2014 paper (5 citations) extends the framework to explicitly incorporate safety constraints, ensuring robots avoid collisions with teammates and opponents during gameplay. While his citation counts reflect a focused, early-career impact, Shakiba’s work is notable for bridging classical spline-based path representation with modern swarm intelligence—a practical contribution to the RoboCup and autonomous robotics communities. His research offers a clear, implementable solution for real-time robot navigation under uncertainty.