Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cuNRTO delivers GPU-accelerated solvers for nonlinear robust trajectory optimization via custom CUDA kernels for SOC projections and ADMM, reporting up to 139.6x speedups on unicycle, quadcopter, and manipulator models.
citing papers explorer
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Distributed Covariance Steering via Non-Convex ADMM for Large-Scale Multi-Agent Systems
Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.
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cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
cuNRTO delivers GPU-accelerated solvers for nonlinear robust trajectory optimization via custom CUDA kernels for SOC projections and ADMM, reporting up to 139.6x speedups on unicycle, quadcopter, and manipulator models.