Hierarchical Ensembles of Autonomous Decision Systems for Satellite Swarms
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Solution Overview
Problem
The increasing complexity of small satellite systems and swarms in low Earth orbit requires advanced autonomy to manage multiple signals and subsystems efficiently, with existing autonomous systems facing challenges in covering all possible combinations and handling unpredictable operating environments, leading to impractical design and performance degradation.
Innovation Solution
A Hierarchical Ensembles of Autonomous Decision Systems (HEADS) using fuzzy logic and recursive ensemble weighting, which enables multi-layered logic for granular control and decision-making, incorporating inferred information and minimizing per-expert complexity, allowing for robustness under uncertain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional autonomous systems are used to manage satellite systems, then basic automation is achieved, but the system cannot cover all possible combinations and performs poorly in unpredictable environments
Solution Approach 1:
The autonomous decision system is divided into multiple expert systems, each specialized in handling specific subsets of input space or particular types of decisions. This segmentation allows each expert to develop deep expertise in its domain while collectively covering the entire decision space, improving adaptability to diverse and unpredictable environments without sacrificing reliability through comprehensive coverage
Solution Approach 2:
The system dynamically selects and weights expert opinions based on current operating conditions. The weighting mechanism adjusts in real-time to emphasize experts whose expertise matches the current situation, enabling the system to adapt to unpredictable environments while maintaining reliable decision-making through context-appropriate expert selection
2Reliability
If comprehensive logic rules are designed to cover all possible combinations, then complete decision coverage is achieved, but the design becomes impractical due to exponential complexity
Solution Approach 1:
Instead of creating a single comprehensive rule set that would require exponential numbers of rules, the system segments the decision space into multiple expert systems, each handling a specific subset. This reduces the complexity of individual experts while collectively achieving complete decision coverage across all possible input combinations
Solution Approach 2:
Each expert system handles only a partial portion of the total decision space rather than requiring complete coverage from a single system. The collective ensemble of experts provides excessive coverage that ensures all scenarios are addressed while keeping individual expert complexity manageable through focused scope
3Productivity
If more spacecraft are added to swarms to enhance data products, then spatial and temporal resolution improve, but the complexity of managing these networks increases
Solution Approach 1:
The network management function is segmented into distributed autonomous decision systems at each spacecraft, eliminating the need for centralized control of every individual unit. Each spacecraft's expert system independently manages its own operations and coordinates with others, reducing overall network management complexity while maintaining high data product quality through coordinated swarm operations
Solution Approach 2:
Each spacecraft in the swarm is equipped with autonomous decision-making capabilities that enable self-service operations. The expert systems on individual spacecraft autonomously handle decision-making without requiring constant ground intervention, reducing the burden on ground operators and infrastructure while the swarm collectively achieves enhanced data products through coordinated efforts
4Ease of operation
If ground operators manage large networks of spacecraft, then centralized control is maintained, but the burden on ground infrastructure and operators becomes unsustainable
Solution Approach 1:
The system transitions from static centralized control to dynamic distributed autonomy. Ground operators shift from directly managing individual spacecraft to overseeing the autonomous expert systems that manage spacecraft operations. This dynamic redistribution of control reduces ground operator burden while increasing on-board autonomy through intelligent autonomous decision-making capabilities
Solution Approach 2:
Spacecraft are equipped with self-service autonomous decision systems that independently manage their operations without requiring constant ground intervention. The expert systems handle routine decisions and adapt to changing conditions autonomously, dramatically reducing the burden on ground operators and infrastructure while maintaining high levels of on-board autonomy for sustainable swarm operations
Data Source
AI summary
A Hierarchical Ensembles of Autonomous Decision Systems (HEADS) system with a recursive ensemble weighting update is proposed. The system is built on fuzzy logic leading to an understandable and tractable logic design that leverages subject matter experts to design system operations. The hierarchical structure enables multi-layered logic for granular control and decisions incorporating inferred information. The control output from each ensemble is a mixture from independently trained fuzzy systems processed through a gating network. The gating network weights are updated recursively. Each expert uses a subset of the input space to minimize per-expert complexity and support ensemble robustness under uncertain or evolving state realizations and operating environments. Finally, autonomy based on fuzzy systems offers the potential for increased human comprehension of an agent's status and decision logic.