TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910221020954624 |
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| author | Mahajan, Ishaan Arrizabalaga, Jon Grillo, Andrea Vega, Fausto Anderson, James Manchester, Zachary Plancher, Brian |
| author_facet | Mahajan, Ishaan Arrizabalaga, Jon Grillo, Andrea Vega, Fausto Anderson, James Manchester, Zachary Plancher, Brian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics Mahajan, Ishaan Arrizabalaga, Jon Grillo, Andrea Vega, Fausto Anderson, James Manchester, Zachary Plancher, Brian Robotics Systems and Control Optimization and Control 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. |
| title | TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics |
| topic | Robotics Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2605.13748 |