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About

Xiaohan Wang is a leading researcher in multi-robot systems and swarm intelligence, with a focus on decentralized coordination, formation control, and autonomous navigation. Their work bridges theoretical algorithms and practical deployment, addressing key challenges in cooperative pursuit, formation maneuvering, and low-cost localization. Wang’s most cited paper, "Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning" (2022, 46 citations), introduces a hybrid algorithm that combines reinforcement learning with potential fields to enable decentralized multi-robot hunting of evaders using only local observations—a significant advance in scalable swarm coordination. Other notable contributions include a low-cost SLAM algorithm for last-mile indoor delivery (2019, 11 citations), enhancing autonomous navigation in complex environments, and "Enhanced Affine Formation Maneuver Control Using Historical Velocity Command" (2023, 9 citations), which improves multi-vehicle system performance by leveraging historical data without altering network connectivity. Wang also developed optiSwarm (2022, 6 citations), an optical swarm robot platform using implicit cooperation, and a triangular lattice formation algorithm (2023, 3 citations) that maintains performance from simulation to real-world multi-robot scenarios. With a growing citation impact, Wang’s work is foundational for advancing autonomous swarm systems in logistics, surveillance, and exploration.