TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
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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BibTeX
@misc{mahajan2026tinysdp,
title = {{TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics}},
author = {Ishaan Mahajan and Jon Arrizabalaga and Andrea Grillo and Fausto Vega and James Anderson and Zachary Manchester and Brian Plancher},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.13748},
eprint = {2605.13748},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.13748},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
AU - Ishaan Mahajan
AU - Jon Arrizabalaga
AU - Andrea Grillo
AU - Fausto Vega
AU - James Anderson
AU - Zachary Manchester
AU - Brian Plancher
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2605.13748
UR - https://arxiv.org/abs/2605.13748
AB - 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.
ER -