Automations
This pillar addresses autonomous mission workflows for rovers, probes, and exploration systems operating with long communication delays and harsh environmental constraints. Content should show how custom navigation and mission logic can improve resilience, scientific target selection, and fault recovery in advanced aerospace programs.
This foundational page details the custom orchestration of autonomous agents for long-duration, communication-delayed space missions. It explains how a multi-agent architecture combining navigation, science, and fault management logic improves resilience, scientific yield, and operational uptime, directly reducing ground control burden and mission risk for aerospace programs.
This page covers the custom workflow for spacecraft to autonomously calculate their position and execute station-keeping maneuvers using onboard sensors and celestial references. It details the architecture for fusing star tracker, GPS, and ranging data, and the business impact of reducing ground-based tracking operations and fuel consumption for satellite operators.
This page explains the custom agentic system that ingests conjunction data messages (CDMs), models debris trajectories, and autonomously plans and executes fuel-optimal evasive maneuvers. It focuses on the architecture for real-time risk assessment, maneuver validation, and the critical reduction of operator workload and collision risk in congested orbits.
This page details the workflow for autonomously calculating and executing mid-course corrections for deep-space probes, accounting for navigation uncertainties and propulsion constraints. It covers the integration of optical navigation, orbit determination agents, and thruster control logic to ensure mission accuracy while minimizing ground-in-the-loop delays.
This page outlines the custom multi-agent workflow for spacecraft to autonomously approach, inspect, and dock with another object in space. It explains the sensor fusion, relative navigation, and closed-loop control architecture required, highlighting the operational savings and mission-enabling potential for on-orbit servicing and assembly.
This page covers the high-stakes automation workflow for autonomously guiding a vehicle through planetary entry, using real-time sensor data to adjust trajectory and trigger parachute/retro-rocket events. It details the fault-tolerant control architecture and the business imperative of eliminating communication blackout periods to ensure landing success.
This page describes the custom workflow for spacecraft to determine attitude and position solely by identifying stars and planets, critical for GPS-denied environments. It explains the onboard image processing, star catalog matching, and Kalman filtering agents, and their role in reducing dependency on ground-based navigation support.
This page details the automation for planetary rovers or landers to process stereo imagery and LIDAR in real-time to identify safe traverse paths or landing zones. It covers the perception pipeline, hazard mapping agents, and path planning logic that enable higher daily drive distances and prevent mission-ending immobilizations.
This page explains the workflow where an onboard science agent prioritizes targets (e.g., rock formations, atmospheric phenomena) based on pre-defined goals and real-time context. It outlines the architecture for balancing resource constraints with science value, dramatically increasing data quality per communication pass for remote exploration missions.
This page covers the custom orchestration layer that dynamically schedules instrument operations, data processing, and communication tasks based on power, thermal, and time constraints. It details the agent-based optimization and the operational upside of maximizing asset utilization without constant ground-based re-planning.
This page focuses on the workflow for specific scientific instruments, where an agent manages calibration, exposure times, and observation sequences autonomously. It explains integration with platform pointing systems and the business benefit of capturing opportunistic science events that would be missed under manual, ground-scheduled operations.
This page details the automation for prospecting robots to analyze regolith composition, plan excavation sites, and schedule processing plant operations to produce water or oxygen. It covers the integration of spectral analysis, geometric planning, and the economic case for reducing Earth-supplied consumables for sustained presence missions.
This page explains the custom workflow to autonomously align, calibrate, and fuse data streams from disparate instruments (e.g., cameras, spectrometers, radar) into a coherent environmental model. It details the correlation agents and the value of producing higher-fidelity situational awareness for both navigation and science decision-making.
This page covers the onboard processing pipeline that analyzes hyperspectral or gamma-ray data to identify mineralogical or compositional anomalies in real-time. It outlines the lightweight ML models and the agent that flags high-priority findings for immediate downlink, optimizing limited bandwidth for the most valuable science.
This page details the workflow for monitoring engineering telemetry and external sensor data (e.g., radiation, debris impacts) to detect system threats or environmental anomalies. It explains the architecture of detection agents, alert triage, and the operational benefit of early warning for fault management and crew safety systems.
This page outlines the custom workflow that uses telemetry history and physics-based models to predict component failures (e.g., reaction wheels, pumps) before they occur. It covers the anomaly detection agents, root-cause analysis logic, and the significant cost avoidance from preemptive maintenance or reconfiguration.
This page explains the high-autonomy workflow where, upon detecting a fault, agents identify redundant pathways, reconfigure software, and re-route data or power to restore functionality. It details the architecture for system modeling and safe reconfiguration, critical for maintaining operations during long communication blackouts.
This page covers the predefined, agent-driven sequence for entering a minimum-power safe mode during a critical fault and then autonomously executing a recovery checklist. It focuses on the state machine logic, health monitoring integration, and the business impact of reducing recovery time from days to hours without ground intervention.
This page details the workflow where an onboard agent tags data with science/engineering value scores, compresses it, and schedules transmissions to optimize limited ground station contact windows. It explains the optimization algorithms and the direct link to maximizing the scientific return on investment per mission.
This page explains the custom automation for managing data bundles across a sporadically connected network of orbiters, landers, and relays. It covers the bundle protocol agents, custody transfer logic, and the architecture that enables reliable, store-and-forward communications in deep space, reducing data loss.
This page outlines the workflow where raw sensor data is processed into higher-level insights (e.g., 'detected water signature in crater') before downlink. It details the pipeline of ML models and summarization agents, highlighting the bandwidth savings and accelerated ground science analysis.
This page covers the custom workflow for a rover or orbital robot to plan collision-free arm motions to acquire samples or perform maintenance, using onboard vision. It explains the motion planning agents, force-torque feedback integration, and the time savings over ground-in-the-loop, step-by-step commanding.
This page details the multi-agent orchestration for two or more robotic systems to collaboratively assemble structures or handle large payloads in space. It covers task allocation, relative pose coordination, and the architecture enabling in-space manufacturing and infrastructure building without constant human oversight.
This page explains the end-to-end automation for a rover to select a rock core, drill, extract, seal, and cache the sample within an onboard storage system. It details the sequence of vision, manipulation, and sealing agents, critical for complex sample-return mission phases operated with high latency.
This page outlines the workflow that models future power generation (solar) and consumption (instruments, heaters) to proactively manage battery state of charge. It explains the forecasting agents and the control logic that prevents brownouts, enabling more aggressive science campaigns without risking spacecraft safety.
This page covers the custom control system that continuously adjusts solar panel orientation to maximize energy capture while avoiding obstructions or thermal limits. It details the integration with attitude control and the measurable improvement in daily energy budget, directly increasing available power for payloads.
This page details the automation for managing heaters, louvers, and radiators to maintain component temperatures within safe ranges despite wildly varying external environments. It explains the model-predictive control agents and their role in reducing heater power waste and preventing thermal-induced failures.
This page explains the distributed workflow for a fleet of satellites to autonomously maintain their relative positions and orbital slots. It covers the inter-satellite communication, collaborative decision-making agents, and the operational cost savings from automating fleet management for telecom or Earth observation constellations.
This page details the custom automation for planning and adjusting efficient, long-duration trajectories for electric propulsion systems. It covers the integration of navigation estimates with propulsion models, enabling missions to reach distant targets with minimal fuel, a key economic driver for commercial and scientific spaceflight.
This page outlines the science-driven workflow where onboard vision models analyze terrain imagery to classify rock types, stratigraphy, or erosional features, and flag them for closer inspection. It details the ML pipeline and the agentic loop that increases the rate of high-value geological discovery per mission sol.
This page covers the closed-loop automation where preliminary data from one instrument (e.g., a spectrometer) triggers an immediate, detailed measurement with another (e.g., a close-up imager). It explains the rapid decision agents and the architecture that captures ephemeral science opportunities impossible with ground-based sequencing.
This page details the workflow for a spacecraft to predict ground station visibility, negotiate link parameters (data rate, frequency), and autonomously establish connections. It covers the scheduling agents and protocol handlers that maximize data throughput and reduce the operational overhead of manual link scheduling.
This page explains the automation for receiving, decrypting, validating, and routing command sequences from Earth, ensuring they are authentic and safe to execute. It details the security agent architecture and checksum/sequence validation, which is foundational for protecting assets from cyber threats in space.
This page covers the multi-asset workflow where orbiters autonomously serve as relays for landers, routing data based on visibility and link quality. It explains the network-aware agents and the system-level benefit of creating a resilient, mesh-like communications infrastructure for planetary exploration networks.
This page outlines the post-operation workflow where agents compile a structured summary of activities, system performance, and any anomalies encountered during a mission phase. It details the natural language and data aggregation agents that produce actionable reports for ground engineers, speeding up analysis and planning for the next cycle.
This page explains the ground-based automation for running mission software against high-fidelity simulations (digital twins) to validate behaviors before upload. It covers the automated test orchestration, result comparison agents, and the reduction in costly on-orbit debugging and mission delays.
This page details the contingency workflow where, after a partial failure, agents re-plan mission objectives and resource usage to continue operating at a reduced capability. It explains the goal-replanning logic and its critical role in extracting maximum value from a damaged asset, preserving mission ROI.
This page covers the distributed AI workflow for multiple small satellites or drones to maintain a formation, share data, and collaboratively achieve a goal (e.g., synthetic aperture radar). It details the consensus algorithms and relative navigation agents that enable scalable, resilient swarm operations for science and defense.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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