Evan Greco

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About

Evan Greco is a robotics researcher whose work centers on motion planning, a fundamental challenge in autonomous systems. His most significant contribution, the MARRT (Medial Axis biased rapidly-exploring random trees) algorithm, addresses a critical gap in sampling-based path planning: generating not just feasible trajectories, but paths with desirable properties like safety and efficiency. By biasing exploration toward the medial axis—the set of points equidistant from obstacles—Greco’s approach produces paths that naturally maximize clearance, improving robustness in cluttered environments. This work, published in 2014, has garnered 41 citations, reflecting its influence on subsequent research in safe motion planning. Greco’s innovation highlights a shift from merely finding any path to optimizing for quality, a key concern in real-world robotics applications from warehouse automation to autonomous navigation. His contributions offer a practical framework for researchers seeking to balance computational efficiency with path safety, making his work a valuable reference for students and engineers tackling complex motion planning problems.