Objectives of the Product

The request for new satellite-based observations and data management is largely dependent on centralised ground-based operations and constrained by the availability of the ground station, where data and commands are exchanged. These data are only made available for further analysis after many hours.

This limits the possibility to implement approaches such as ‘tip and cue’, since the ground segment needs to plan monitoring acquisitions, wait until the data reach a ground station, and only then process and analyse the data. In case something interesting is present in the acquisition (e.g. wild fire, dark vessel or illegal phishing), a follow-up acquisition can then be planned to gather more data, but only hours later.

The large delay between these actions limits the effectiveness of the monitoring actions. High-performance computing units and inter-satellite link connections through satellites are planned for many future constellations, and exploiting those assets will dramatically change the operational scenario: Earth observation data can be immediately processed, with AI algorithms used to extract relevant features and support autonomous decisions through AI agents, just using on-board resources and allowing an almost real-time monitoring, which would be impossible otherwise solution using transmitters based on vacuum tubes, like TWT, or a set of discrete solid-state components.

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Customers and their Needs

Leonardo acts as the anchor partner and reference early adopter, with direct involvement in defining the system needs and mission integration constraints, particularly in view of its future Earth observation constellation.

In parallel, Leonardo has initiated preliminary technical discussions with Satlantis (Spain) and D-Orbit (Italy):

• Satlantis, a vertically integrated smallsat operator and payload manufacturer, has expressed their interest in testing on-board autonomy features for responsive Earth observation scenarios. They represent a relevant user profile due to their dual role as integrator and operator of high-resolution optical payloads.
• D-Orbit, as a platform and in-orbit service provider, is evaluating TASCNET for integration on its ION Satellite Carrier as part of future mission architectures with embedded edge computing.

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Targeted customer/users countries

Spain and Italy

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Product description

The TASCNET product will integrate the typical capabilities of the control ground segment with autonomous capabilities allowed by the growing availability of high-performance computing resources installed on the satellite platform and the possibilities offered by AI algorithms and agents.

The main goal of the product is to migrate some of the control functionalities currently performed the ground segment to the space segment. This change allows a constellation with required capabilities to autonomously update the mission plan if specific conditions are found, enabling the possibility to implement complex scenarios, such as ‘tip and cue’, without the need of support from ground operators, greatly reducing the latency.

TASCNET combines the elements from two main components into a hybrid mission control:

• On-board software components. These components are installed on satellites with computing resources able to run complex algorithms required to process the payload data, identify the main features within the image, autonomously decide the following operations, and operate with other satellites of the constellation.
• On-ground control ground segment components. These components have the responsibility to build the mission plan for each satellite of the constellation, but also to control the complex algorithms and the criteria for more complex tasks, such as ‘tip and cue’..

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Added Value

TASCNET activities focus on three main pillars:

• On-board scheduling and autonomous tasking: it enables satellites to autonomously generate observation schedules and dynamically adjust them in orbit to react to new information, ensuring optimal use of platform resources without ground intervention.

• On-board Earth observation processing: TASCNET includes a library of AI models able to process payload data directly in orbit, extracting relevant features (e.g. changes, targets, anomalies). This unlocks new use cases such as intelligent alerting and automated re-tasking.

• Next-generation ground segment: traditional control segments must evolve to integrate hybrid planning logics. TASCNET supports this evolution with a multi-master planning architecture that brings together autonomous onboard planning with strategic ground-based oversight.

We foresee the following main added values:

On-board EO Processing and Autonomous Planning

• Detects opportunities and anomalies in real time, adjusting plans without waiting for ground commands.
• Enables ultra-low-latency reaction chains, such as ‘tip and cue’.
• Reduces bandwidth consumption and operational overheads by prioritising only high-value data for downlink.

AI-based Mission Optimisation

• Intelligent agents.
• Observation planning to improve responsiveness and reduce redundancy.

Hybrid Ground Segment Adaptation

• TASCNET enables coexistence of on-board and on-ground control logic.
• Enhances user interaction with the mission via predictive dashboards and dynamic plan reconciliation tools.

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Current Status

The project has started identifying the most relevant constellation characteristics that can enable added value services, such as ‘tip and cue’ without ground segment assistance, the most relevant use cases and the associated mission planning challenges. The requirements review has been successfully held, and the team is working on the preliminary design review preparation.