From First Principles
to Field Deployment.
Astra Kinetics is built on a foundation of rigorous academic inquiry. Our core IP in VLA models and Compressed Sensing has been validated through peer-reviewed publications at top-tier conferences (ICRA, IEEE, ISME).
Vigil-VLA: Grounded Chain-of-Thought for Explainable Planning and Real-Time Self-Correction
Abstract
Vision-Language-Action (VLA) models often fail in dynamic environments due to their 'black-box' nature. We introduce Vigil-VLA, utilizing a Predictive State Dissonance (PSD) mechanism that compares predicted outcomes against live visual feedback to trigger real-time re-planning. This boosts success rates from <5% to 74% under perturbation.
ASCEND-VLA: Autonomous Skill Composition and Embodied Negotiation
Abstract
Introducing a flywheel framework for multi-agent collaboration. ASCEND-VLA integrates Autonomous Skill Discovery with a Collaborative V-CoT Negotiation protocol, allowing agents to bid on tasks based on capability and synchronize actions in unstructured environments.
Plan-as-Message: A Grounded Negotiation Protocol for Coordinated Manipulation
Abstract
We propose the V-CoT Negotiation Protocol (VNP), where agents broadcast their visual chain-of-thought plans to coordinate complex bimanual tasks. This replaces brittle, centralized schedulers with decentralized, capability-grounded bidding.
Physics-Informed Compressed Sensing: Incorporating Rotor Dynamics for High-Fidelity State Estimation
Abstract
Classical compressed sensing is physics-agnostic. We propose a PICS framework that embeds Lagrangian rotor dynamics directly into the reconstruction optimization. This allows for accurate fault diagnosis from sub-Nyquist samples with 30% compression ratios.
Sign of the Times: Ultra-Low-Power Rotor Fault Diagnosis using 1-Bit Compressed Sensing
Abstract
Challenging the high-resolution sensing paradigm by proposing 1-bit quantization for vibration analysis. We demonstrate that deep neural networks can classify complex rotor faults using only the sign of the vibration signal, drastically reducing power consumption for wireless sensor nodes.
Real-Time Fault Diagnosis in High-Frequency VFDs Using Recurrent Neural Networks
Abstract
A data-driven framework for detecting incipient faults in Variable Frequency Drives (VFDs). Using LSTMs to process raw high-frequency time-series data, effectively identifying IGBT open-circuit faults and capacitor degradation without manual feature extraction.
Thermal Performance Analysis of Photovoltaic Panels with Integrated Microchannel Water Cooling
Abstract
Investigation of active cooling strategies using integrated microchannels. Our 3D FEM model demonstrates a 25°C temperature reduction and a 12-15% increase in electrical efficiency under peak irradiance.
Piezoelectric Energy Harvesting from Aeolian Sand Abrasion on Desert-Based Pillar Arrays
Abstract
A novel energy harvesting concept for remote desert environments. Harnessing the kinetic energy of sand particles via stochastic impact excitation on piezoelectric pillars to power autonomous sensor networks.
Robust H-Infinity Control for Chatter Suppression in High-Speed 5-Axis Machining
Abstract
Addressing the stability of thin-walled workpiece machining. We formulate a robust control law that accounts for varying structural dynamics, ensuring surface finish quality and tool longevity in high-fidelity aerospace manufacturing.
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A comprehensive overview of our core architecture, validation data, and development roadmap is available for investors, strategic partners, and national innovation agencies.