Satellite Scheduling Using Directed Acyclic Graphs
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Solution Overview
Problem
Scheduling systems for constellations of imaging satellites face challenges in efficiently generating near-optimal schedules that account for various constraints, such as pair-wise and cumulative constraints, while also allowing for rapid updates to respond to dynamic requests and maximize resource utilization.
Innovation Solution
A system and method utilizing directed acyclic graphs (DAGs) to schedule events for imaging satellites, which includes a scheduling engine that incorporates user input and dynamically adjusts schedules to avoid constraint violations, optimize resource utilization, and update schedules in near real-time, by analyzing satellite properties and event requests to produce control instructions for the satellites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling algorithms are used to generate satellite schedules, then the schedules can satisfy basic constraints, but the schedules are not near-optimal and fail to maximize resource utilization
Solution Approach 1:
The scheduling problem is segmented into discrete events represented as nodes in a directed acyclic graph (DAG), with constraints represented as edges. This segmentation allows the complex scheduling problem to be broken down into manageable components that can be processed efficiently while still achieving near-optimal solutions.
Solution Approach 2:
The system dynamically updates the DAG-based schedule in near real-time by incorporating user input and changing conditions. The scheduling engine can rapidly recompute schedules by modifying the DAG structure, allowing adaptive response to dynamic requests while maintaining constraint satisfaction.
2Reliability
If the scheduling system incorporates multiple constraints (pair-wise and cumulative), then the schedules become more accurate and feasible, but the computational complexity increases significantly
Solution Approach 1:
The scheduling problem is transformed from a temporal sequence problem into a graphical dimension by representing events as nodes and constraints as edges in a DAG. This dimensional transformation allows multiple constraints (pair-wise and cumulative) to be simultaneously visualized and processed in a unified graphical framework, reducing computational complexity.
Solution Approach 2:
The patent replaces traditional mechanical scheduling algorithms with a graph-theoretic approach using DAGs. This substitution enables efficient processing of multiple constraints by leveraging graph algorithms that can handle complex constraint networks more efficiently than conventional methods.
3Adaptability or versatility
If the scheduling system allows rapid updates to respond to dynamic requests, then the adaptability improves, but the time to generate and update schedules increases
Solution Approach 1:
The system performs preliminary actions by pre-processing event requests and constraints into a DAG structure before actual scheduling is needed. This preliminary organization of data allows rapid updates and queries to be executed efficiently when dynamic requests occur, reducing the time penalty for adaptability.
Solution Approach 2:
The scheduling engine incorporates feedback mechanisms that allow user input and changing conditions to be rapidly integrated into the existing DAG structure. The system can quickly recompute schedules by propagating changes through the graph, enabling near real-time updates while maintaining constraint satisfaction.
Data Source
AI summary
Systems and methods are provided for scheduling objects having pair-wise and cumulative constraints. The systems and methods presented can utilize a directed acyclic graph to increase or maximize a utilization function. The objects can comprise satellites in a constellation of satellites. In some implementations, the satellites are imaging satellites, and the systems and methods for scheduling can use human collaboration to determine events of interest for acquisition of images. In some implementations, dominant edges are removed from the directed acyclic graph. In some implementations, dynamic weights are assigned to nodes associated with downlink events in the directed acyclic graph.