Panagiotis Rousseas

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About

Panagiotis Rousseas is a roboticist whose research lies at the intersection of motion planning, autonomous navigation, and reinforcement learning. His work focuses on enabling robots to move safely and efficiently through constrained or unknown environments. Rousseas is best known for developing provably safe, reactive control strategies that leverage Artificial Potential Fields (APFs) and harmonic functions. In his most cited work, "Optimal Robot Motion Planning in Constrained Workspaces Using Reinforcement Learning" (18 citations), he introduced a novel, deterministic approach that combines APFs with RL to guarantee safety while optimizing motion. He further advanced the field with "Trajectory Planning in Unknown 2D Workspaces: A Smooth, Reactive, Harmonics-Based Approach" (16 citations), which provides provably safe, asymptotically convergent trajectories for robots navigating initially unknown spaces. His research also extends to multi-robot systems, as demonstrated in his work on indoor visual exploration with multi-rotor aerial vehicles, where he developed a two-level control architecture using Artificial-Harmonic Potential Fields. With a growing citation record and a focus on theoretically grounded, practically deployable solutions, Rousseas is contributing to the next generation of intelligent, safety-critical autonomous systems.