How the Air Force Uses AI for Satellite Scheduling & Mission Planning

The United States Air Force has made significant strides in leveraging artificial intelligence (AI) for satellite scheduling and mission planning, as evidenced by the development and implementation of the Managed Intelligent Deconfliction and Scheduling (MIDAS) system. This AI-powered tool has revolutionized the way the Air Force Satellite Control Network (AFSCN) manages its complex satellite communication requests, dramatically improving efficiency and enabling rapid response to changing mission requirements.

The Challenge of Satellite Communication Scheduling

The AFSCN faces a monumental task daily, coordinating approximately 600 satellite communication requests from various users. This process involves managing a global network of antennas and ground support equipment to command and control a diverse array of satellites. The complexity of this scheduling problem stems from several factors:

1. 1. Multiple Satellite Operations Centers (SOCs): Each constellation of satellites (e.g., GPS satellites) is commanded from a separate SOC, which independently submits communication support requests.
   2. Resource Constraints: The limited number of ground stations acts as a bottleneck, with many requests competing for the same resources.
   3. Line of Sight Requirements: Communication can only occur when there is a direct line of sight between the antenna and the satellite.
   4. High-Value Assets: The scheduling process must protect billions of dollars’ worth of on-orbit satellites crucial for national defense.
   5. Conflict Resolution: Typically, more than half of the daily requests conflict with each other, requiring careful deconfliction.

Prior to the introduction of AI-assisted scheduling, this process relied heavily on human schedulers. While these experts were adept at generating high-quality solutions, the process was time-intensive and required extensive training and experience. The increasing demand for satellite communications only exacerbated the strain on these human resources.

MIDAS: AI-Powered Scheduling Solution

To address these challenges, the US Air Force collaborated with Stottler Henke Associates, Inc. to develop MIDAS. This AI tool automates the scheduling of satellite communication requests, incorporating the expertise and insights of human schedulers into its algorithms.

Key Features of MIDAS

1. 1. Two-Stage Processing: MIDAS employs a sophisticated two-step approach to scheduling:
      • Bottleneck Scheduling: This initial stage shuffles tasks within their defined constraints to minimize conflicts.
      • Business Rules Deconfliction: The second stage applies a set of user-definable business rules to relax certain constraints when necessary, mimicking the decision-making process of expert human schedulers.
   2. User-Friendly Interface: MIDAS provides a familiar interface modeled on legacy Electronic Schedule Dissemination (ESD) systems, facilitating easy adoption by existing staff.
   3. Hardware Compatibility: The system runs on standard consumer hardware, reducing implementation costs.
   4. Legacy System Integration: MIDAS communicates with existing systems through a well-defined plain-text file format, allowing seamless import of scheduling requests and export of results.
   5. Rapid Processing: MIDAS can generate a viable schedule in minutes, a task that previously required hours of human effort.
   6. “What-If” Scenario Capability: The speed of MIDAS enables planners to quickly assess the impact of potential events, outages, or mission changes.

AI Algorithms in MIDAS

The core of MIDAS’s AI capabilities lies in its advanced algorithms for bottleneck scheduling and business rules application.

Bottleneck Scheduling Algorithm

This single-pass algorithm aims to minimize inter-support conflicts while adhering to user-specified constraints. Key aspects include:

1. 1. Intelligent Task Ordering: MIDAS mimics human expert heuristics in determining the order in which to process tasks. For example, it prioritizes scheduling Low-Earth-Orbit (LEO) contacts before High-Earth Orbit (HEO) or Geosynchronous (GEO) contacts due to their shorter visibility windows and reduced flexibility.
   2. Flexibility Assessment: The algorithm considers multiple dimensions of flexibility, including temporal flexibility, resource contention, and the current state of scheduled tasks.
   3. Bottleneck Avoidance: A preprocessor identifies potential bottlenecks by analyzing resource contention across time windows. This global perspective informs the prioritization of tasks and resource allocation decisions.
   4. Pseudo-Probabilistic Allocation: Requests are initially “spread” across potential resources and time windows to provide a comprehensive view of resource utilization.

Business Rules Deconfliction

When bottleneck scheduling cannot resolve all conflicts, MIDAS employs a sophisticated business rules engine to suggest modifications to support requests. This process mirrors the decision-making of expert human schedulers:

1. 1. Rule-Based Approach: MIDAS incorporates hundreds of constellation-specific rules, capturing the nuanced knowledge of experienced schedulers.
   2. Iterative Conflict Resolution: The system examines each remaining conflict and suggests changes based on predefined rules and preferences.
   3. Adaptive Modifications: Suggestions may include reducing preparation time, moving supports to different time windows or sites, replacing ground support equipment, or even dropping certain hardware requirements.
   4. Constellation-Specific Logic: The rule base considers the specific needs and flexibilities of different satellite constellations.
   5. User-Editable Rules: The system allows for ongoing refinement and updating of the rule base to adapt to changing requirements or new insights.

Benefits and Impact of AI-Assisted Scheduling

The implementation of MIDAS has brought about significant improvements in the AFSCN’s scheduling capabilities:

1. 1. Efficiency Gains: MIDAS can produce a conflict-free schedule in less than an hour, often in under 15 minutes, compared to the previous process that required eight or more expert schedulers.
   2. Reduced Manual Workload: The system automates the checking and adjustment of schedule requests, a task that previously required manual intervention for approximately half of all requests.
   3. Consistency: AI-driven scheduling eliminates inconsistencies that could arise from human variability in the scheduling process.
   4. Rapid Response Capability: The speed of MIDAS enables quick rescheduling in response to vehicle emergencies or other unexpected events.
   5. Enhanced Planning Capabilities: The system’s “what-if” scenario functionality allows planners to assess the impact of potential changes or disruptions rapidly.
   6. Resource Optimization: By efficiently managing the bottleneck resources (ground stations), MIDAS helps maximize the communication capability of the satellite network.
   7. Training Support: The system can be used to train new schedulers, providing a platform for them to understand complex scheduling decisions without risking operational disruptions.

Future Implications and Potential Expansions

The success of MIDAS in satellite communication scheduling opens up several avenues for future development and application:

1. 1. Expanded AI Integration: As AI technologies continue to advance, more sophisticated machine learning algorithms could be incorporated to further refine the scheduling process and predict potential conflicts or resource bottlenecks.
   2. Cross-Domain Application: The principles and algorithms developed for MIDAS could be adapted to other complex scheduling problems within the military or civilian sectors.
   3. Dynamic Mission Planning: The rapid scheduling capabilities of MIDAS could be integrated into broader mission planning systems, allowing for more agile and responsive space operations.
   4. Autonomous Operations: Future iterations could move towards more autonomous satellite operations, with AI systems not just scheduling communications but also making real-time decisions about satellite maneuvers and resource allocation.
   5. Cybersecurity Enhancement: AI could be leveraged to identify and mitigate potential security risks in satellite communications scheduling, ensuring the integrity and confidentiality of critical space operations.

Conclusion

The Air Force’s adoption of AI for satellite scheduling and mission planning, as exemplified by the MIDAS system, represents a significant leap forward in space operations management. By automating complex scheduling tasks, incorporating expert knowledge into AI algorithms, and enabling rapid scenario planning, systems like MIDAS are enhancing the efficiency, responsiveness, and capabilities of military satellite operations.

As space continues to be a critical domain for national security and global communications, the role of AI in managing these assets is likely to grow. The success of MIDAS demonstrates the potential for AI to tackle complex, time-sensitive problems in military operations, paving the way for further innovations in space technology and mission planning.

The integration of AI into satellite scheduling not only addresses current operational challenges but also positions the Air Force to better manage the increasing complexity of space operations in the future. As more satellites are launched and space becomes an increasingly contested domain, the ability to rapidly adapt and optimize resource utilization will be crucial. AI-driven systems like MIDAS will play a pivotal role in ensuring that the Air Force can maintain its strategic advantage in space operations, supporting national security objectives and enabling more effective global military operations.