CA-HCBF creates a unified acceleration-level safety framework for mixed holonomic and nonholonomic robots and allocates avoidance duties proportionally to each robot's capability using a support-function metric and clipping.
Osqp: An operator splitting solver for quadratic programs
5 Pith papers cite this work. Polarity classification is still indexing.
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Conic-TinyMPC adds second-order cone support and code generation to TinyMPC, delivering up to 142.7x speedup on embedded QP and SOCP problems for model-predictive control.
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.
A distributed optimization controller uses truncation functions and two-time-scale auxiliary variables to guarantee collision avoidance, connectivity preservation, and target convergence for multi-agent systems under time-varying communication topologies.
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
citing papers explorer
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Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
CA-HCBF creates a unified acceleration-level safety framework for mixed holonomic and nonholonomic robots and allocates avoidance duties proportionally to each robot's capability using a support-function metric and clipping.
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Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC
Conic-TinyMPC adds second-order cone support and code generation to TinyMPC, delivering up to 142.7x speedup on embedded QP and SOCP problems for model-predictive control.
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HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.
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Distributed Safety-Critical Control of Multi-Agent Systems with Time-Varying Communication Topologies
A distributed optimization controller uses truncation functions and two-time-scale auxiliary variables to guarantee collision avoidance, connectivity preservation, and target convergence for multi-agent systems under time-varying communication topologies.
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GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.