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About

Duong Le is a leading researcher in robotics, specializing in multi-robot motion planning, sampling-based algorithms, and task-and-motion planning under complex dynamics. His work addresses the fundamental challenge of enabling teams of robots with differential constraints—such as acceleration limits and nonholonomic steering—to navigate cluttered environments efficiently. Le’s most influential contribution, “Multi-Robot Motion Planning With Dynamics via Coordinated Sampling-Based Expansion Guided by Multi-Agent Search” (38 citations), introduces a centralized approach that combines sampling-based tree expansion with multi-agent search to solve high-dimensional, dynamic motion-planning problems. He further advanced the field with “Guiding sampling-based tree search for motion planning with dynamics via probabilistic roadmap abstractions” (13 citations), which leverages workspace decomposition to guide state-space search for nonlinear systems. Le also developed INTERACT, an interactive search framework coupling action and motion planning for mobile robots. His work on unlabeled-goal multi-robot planning, where robots must reach unspecified goal locations, demonstrates his ability to tackle combinatorial complexity while ensuring dynamically feasible trajectories. With a consistent focus on bridging theoretical planning algorithms with practical robotic constraints, Duong Le’s research has become essential reading for those working on autonomous navigation, multi-agent coordination, and motion planning under real-world physical limits.