https://plancherb1.github.io/authors/andreagrillo/ Recent content on Brian Plancher Hugo -- gohugo.io en-us © {year} Brian Plancher Tue, 08 Sep 2026 00:00:00 +0000 https://plancherb1.github.io/authors/andreagrillo/ Tue, 08 Sep 2026 00:00:00 +0000 https://plancherb1.github.io/authors/andreagrillo/ https://plancherb1.github.io/publication/accelmpc/ Tue, 08 Sep 2026 00:00:00 +0000 https://plancherb1.github.io/publication/accelmpc/ AccelMPC pairs a co-designed FPGA-accelerated alternating direction method of multipliers (ADMM)-based MPC solver with a custom 6g PCB, providing high-bandwidth communication for deployment on a 35g Crazyflie. Hardware experiments demonstrate 1 kHz onboard constrained MPC with dynamic obstacles, up to 15.6x faster solve times and 195.4x improvement in energy-delay product over state-of-the-art embedded microcontroller-based solvers, all while scaling to optimization problems with over 20,000 optimization variables and a comparable number of constraints. We release our PCB design files, firmware, and FPGA solver code open source. https://plancherb1.github.io/publication/tinysdp/ Mon, 13 Jul 2026 00:00:05 +0000 https://plancherb1.github.io/publication/tinysdp/ We introduce TinySDP, the first semidefinite programming solver designed for embedded systems, enabling real-time model-predictive control (MPC) with formal safety guarantees 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 certifiable semidefinite constraints can be enforced at real-time rates for agile embedded robotics.