Andrew Fishberg

American Institute of Aeronautics and Astronautics, Massachusetts Institute of Technology, Lawrence Berkeley National Laboratory

Papers

Total Citations

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About

Andrew Fishberg’s research lies at the intersection of multi-robot systems, state estimation, and resilient localization, with a focus on enabling teams of robots to operate in GPS-denied or communication-constrained environments. His major contributions center on developing certifiably correct algorithms for range-aided simultaneous localization and mapping (RA-SLAM) and novel systems for inter-agent relative pose estimation using ultra-wideband (UWB) sensors. In his most-cited work, “Multi-Agent Relative Pose Estimation with UWB and Constrained Communications” (30 citations), he introduced a system that allows robots to estimate each other’s 2D poses without external infrastructure, even under limited communication. His 2024 paper, “Certifiably Correct Range-Aided SLAM” (14 citations), is the first to efficiently compute provably optimal solutions to RA-SLAM, a critical step for safe and reliable navigation. Fishberg extended this work to 3D environments in “MURP” (9 citations), further advancing multi-agent localization. Beyond technical contributions, he co-developed a hands-on middle-school robotics program at MIT, demonstrating a commitment to broadening STEM participation. His work is foundational for robust, scalable multi-robot autonomy.