Nested Genetic Algorithm for Swarm Trajectory Optimization
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Solution Overview
Problem
Current methods lack a framework for safely operating a swarm of spacecraft in close proximity during in-space construction and assembly, posing risks of collision and debris creation due to the lack of matured technology and focused funding, especially in dynamically changing orbital environments.
Innovation Solution
A swarm control system utilizing a nested genetic algorithm that computes and adjusts trajectories for chaser spacecraft to avoid collisions, maintain orbit stability, and accommodate changes in the swarm's size and shape, incorporating guidance genetic algorithms and sensor fusion Kalman filters for real-time collision detection and trajectory adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive safety methods are used for satellite swarms, then collision risk is reduced for free orbital trajectories, but trajectory flexibility and mission task execution are limited
Solution Approach 1:
The patent implements dynamic trajectory optimization using genetic algorithms that continuously adapt satellite trajectories based on real-time swarm configuration and mission requirements. The system transitions from static passive safety to dynamic active optimization, allowing trajectories to be modified in response to changing conditions while maintaining safety constraints through the outer genetic algorithm's collision detection and avoidance mechanisms.
Solution Approach 2:
The system changes orbital parameters (position, velocity, trajectory shape) through genetic algorithm optimization to achieve both safety and mission objectives. The nested genetic algorithms optimize multiple parameters simultaneously - inner algorithms optimize individual satellite trajectories while the outer algorithm optimizes the entire swarm configuration, enabling flexible adaptation without compromising collision avoidance.
2Manufacturing precision
If nested genetic algorithms are used for trajectory optimization, then trajectory accuracy and collision avoidance are improved, but computational complexity increases
Solution Approach 1:
The patent segments the optimization problem into nested hierarchical levels: inner genetic algorithms optimize individual satellite trajectories independently, while the outer genetic algorithm optimizes the overall swarm configuration. This segmentation allows parallel computation of individual trajectories and reduces the computational burden compared to optimizing all satellites simultaneously as a single monolithic problem.
Solution Approach 2:
The inner genetic algorithms perform preliminary optimization of individual satellite trajectories before the outer algorithm integrates them into the swarm configuration. This preliminary action pre-computes feasible trajectories that satisfy individual mission requirements, reducing the search space for the outer algorithm and improving overall computational efficiency.
3Adaptability or versatility
If swarm size and configuration are dynamically changed, then mission adaptability is improved, but collision risk and control difficulty increase
Solution Approach 1:
The system implements feedback mechanisms where the outer genetic algorithm continuously monitors swarm configuration and collision risks, adjusting individual satellite trajectories (through inner genetic algorithms) in response to detected risks. This closed-loop control enables the swarm to dynamically reconfigure while maintaining safety through real-time collision detection and trajectory adjustment.
Data Source
AI summary
A control system includes a target spacecraft and a swarm of chaser spacecraft. Each chaser spacecraft is controlled to follow a corresponding computed trajectory. The system also includes at least one computing device that executes a nested genetic algorithm. The nested genetic algorithm includes multiple guidance genetic algorithms and an outer genetic algorithm. Characteristically, each chaser spacecraft has an associated guidance genetic algorithm that determines a computed trajectory for the chaser spacecraft associated therewith. Advantageously, the outer genetic algorithm checks for collisions and is configured to alter one or more computed trajectories to avoid collisions.