analiza aiqbench
TinySDP introduces a specialized semidefinite programming solver capable of running real-time model-predictive control on resource-constrained microcontrollers, addressing the computational bottleneck that previously prevented rigorous geometric guarantees in agile robotics. By integrating positive-semidefinite cone projections with an a posteriori rank-1 certificate, the system ensures collision-free navigation even in complex scenarios like cul-de-sacs where local methods fail. This advancement demonstrates that high-fidelity convex relaxations can be enforced at real-time rates on embedded hardware, significantly improving path efficiency for agile drones and similar systems.
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
arXiv:2605.13748v2 Announce Type: replace 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.
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.
Accepted to Robotics: Science and Systems (RSS) 2026. 11 pages, 5 figures, 2 tables. Project website: this https URL
Robotics (cs.RO); Systems and Control (eess.SY); Optimization and Control (math.OC)
arXiv:2605.13748 [cs.RO]
(or arXiv:2605.13748v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.13748 Focus to learn more arXiv-issued DOI via DataCite
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