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ARXIV:2603.10711 · AUTONOMOUS SYSTEMS OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.10711AUTONOMOUS SYSTEMS OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems.
Opportunity summary
Pain A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the utilization of massively parallel computing…
Real-time trajectory optimization for nonlinear constrained autonomous systems is critical and typically performed by CPU-based sequential solvers. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
Autonomous Systems Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
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A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems.
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Paper Pack
10.48550/arXiv.2603.10711A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems.
Abstract
Real-time trajectory optimization for nonlinear constrained autonomous systems is critical and typically performed by CPU-based sequential solvers. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the utilization of massively parallel computing architectures like GPUs. To bridge this gap, we introduce a fully GPU-native trajectory optimization framework that combines sequential convex programming with a consensus-based alternating direction method of multipliers. By applying a temporal splitting strategy, our algorithm decouples the optimization horizon into independent, per-node subproblems that execute massively in parallel. The entire process runs fully on the GPU, eliminating costly memory transfers and large-scale sparse factorizations. This architecture naturally scales to multi-trajectory optimization. We validate the solver on a quadrotor agile flight task and a Mars powered descent problem using an on-board edge computing platform. Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12-core CPU baseline. Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz. Furthermore, we demonstrate the solver's extensibility to robust Model Predictive Control by jointly optimizing dynamically coupled scenarios under stochastic disturbances, enabling scalable and safe autonomy.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 8.0
PROBLEM
A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the utilization of massively parallel computing architect...
METHOD
Real-time trajectory optimization for nonlinear constrained autonomous systems is critical and typically performed by CPU-based sequential solvers. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the utiliza...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
WHY NOW
Autonomous Systems Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12-core CPU baseline.
Explicitly stated in the abstract with clear numeric comparison
partial
Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12-core CPU baseline.
Explicitly stated in the abstract with clear numeric evidence
partial
Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
Explicitly stated in the abstract with clear numeric evidence
partial
Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
Explicitly stated in the abstract with clear numeric evidence
partial
To bridge this gap, we introduce a fully GPU-native trajectory optimization framework that combines sequential convex programming with a consensus-based alternating direction method of multipliers.
Directly stated in the abstract as a core methodological contribution
partial
By applying a temporal splitting strategy, our algorithm decouples the optimization horizon into independent, per-node subproblems that execute massively in parallel.
Directly stated in the abstract as a key technical approach
partial
The entire process runs fully on the GPU, eliminating costly memory transfers and large-scale sparse factorizations.
Directly stated in the abstract as a key architectural feature
partial
Furthermore, we demonstrate the solver's extensibility to robust Model Predictive Control by jointly optimizing dynamically coupled scenarios under stochastic disturbances, enabling scalable and safe autonomy.
Directly stated in the abstract as a demonstrated capability
partial
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Concepts
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A GPU-native trajectory optimization framework for real-time nonlinear control in autonomous systems.
Segment
Autonomous Systems Optimization
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Commercial read
8.0/10 public viability
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next verification path
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stale
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passport absent
stale
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Artifact maturity
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Technical feasibility
partial
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Gaps
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Evidence
0 references, 0 sources, 17% evidence coverage.
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ARTIFACTS
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DEFENSIBILITY
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BUZZ
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