Evan Behar

Papers

Total Citations

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About

Evan Behar’s research lies at the intersection of computational geometry and mobile robotics, with a particular focus on enabling safe autonomous navigation in hazardous environments. His most influential work centers on the efficient computation of Minkowski sums—a fundamental geometric operation used in collision detection and path planning. In his 2012 paper “Dynamic Minkowski sums under scaling” (6 citations), Behar developed algorithms that allow these sums to be recomputed efficiently as objects change size, a critical capability for robots adapting to dynamic terrains. His earlier 2011 work on “Fast and robust 2D Minkowski sum using reduced convolution” (3 citations) introduced a novel method that balances speed and accuracy, directly addressing the computational bottlenecks that limit real-time robotic operations. Behar’s contributions are particularly relevant to military applications such as surveillance and casualty extraction, where robots must rapidly detect terrain types and adjust their control strategies to avoid hazards. While his citation counts reflect a focused, specialized impact, his algorithms have provided foundational tools for researchers working on motion planning in unpredictable environments. Behar’s work exemplifies how theoretical advances in geometry can translate into practical solutions for life-saving robotic systems.