TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics

• URL: http://arxiv.org/abs/2605.13748v1
• Date: Wed, 13 May 2026 16:30:32 GMT
• Title: TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
• Abstract summary: Semidefinite cul (SDP) provides a principled framework for convex relaxations in motion planning.<n>Tiny-Riccati-based computational structure solver for embedded dynamic tractability.
• Score: 18.604754784265253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computationally expensive for real-time control, particularly on resource-constrained embedded systems. To address this gap, we introduce TinySDP, the first semidefinite programming solver designed for embedded systems, enabling real-time model-predictive control (MPC) on microcontrollers for problems with nonconvex obstacle constraints. Our approach integrates positive-semidefinite cone projections into a cached-Riccati-based ADMM solver, leveraging computational structure for embedded tractability. We pair this solver with an a posteriori rank-1 certificate that converts relaxed solutions into explicit geometric guarantees at each timestep. On challenging benchmarks, e.g., cul-de-sac and dynamic obstacle avoidance scenarios that induce failures in local methods, TinySDP achieves collision-free navigation with up to 73% shorter paths than state-of-the-art baselines. We validate our approach on a Crazyflie quadrotor, demonstrating that semidefinite constraints can be enforced at real-time rates for agile embedded robotics.

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