Brian Angulo

Moscow Institute of Physics and Technology

Papers

Total Citations

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About

Brian Angulo is a robotics researcher specializing in motion planning for autonomous systems, with a focus on non-holonomic robots operating in complex, dynamic environments. His work addresses one of the field’s most persistent challenges: enabling robots to navigate safely among both static and dynamic obstacles while respecting kinematic constraints. Angulo’s most impactful contribution, the 2022 paper “Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles,” has garnered 21 citations for its novel approach to kinodynamic motion planning. By decomposing this difficult problem into manageable sub-problems and using policy optimization to learn adaptive primitives, his method offers a flexible solution where universal algorithms have fallen short. In earlier work, Angulo empirically evaluated Theta*-RRT and GRIPS algorithms for constructing kinematically feasible paths for wheeled mobile robots, providing valuable comparative insights for practitioners. His research bridges the gap between theoretical motion planning and real-world robotic deployment, making him a rising voice in autonomous navigation. Angulo’s contributions are particularly relevant for students and engineers developing robots for dynamic, unpredictable settings such as warehouses, autonomous vehicles, or search-and-rescue operations.