Spacecraft Image Acquisition Planning Method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for planning image acquisition by spacecraft are inefficient in handling increasing numbers of requests with complex constraints, particularly in meeting local and cumulative constraints, and fail to optimize for stereo, tri-stereo, or multispectral image acquisitions in pushbroom mode.
Innovation Solution
A method that determines discrete acquisition opportunities, groups them, ranks them by start date, evaluates kinematic compatibility, and determines an optimum sequence with maximum weight while respecting local and cumulative constraints, including memory, power, and operational time constraints, to efficiently plan image acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heuristic optimization techniques (e.g., greedy algorithms) are used to solve the planning problem, then high-quality approximation solutions can be provided, but computation times are not satisfactory considering the volume of requests to be processed
Solution Approach 1:
The patent segments the planning problem into discrete acquisition opportunities, each with associated constraints and weights. By breaking down the complex optimization problem into manageable discrete units that can be independently evaluated and ranked, the system achieves both solution quality and computational efficiency.
2Loss of time
If the number of satellites is increased to reduce access time to areas, then satellite access to an area within much shorter timeframes is achieved, but the acquisition plan must be updated within short timeframes which increases computational complexity
Solution Approach 1:
The patent implements a dynamic planning system where discrete acquisition opportunities are continuously evaluated and ranked based on current constraints and weights. This dynamic approach allows the system to adapt rapidly to changing conditions and multiple satellite configurations, maintaining computational efficiency even as the number of satellites increases.
3Reliability
If current methods are used to manage local and cumulative constraints, then some techniques allow cumulative constraints to be taken into account, but none allows the acquisition of stereo, tri-stereo or multispectral images to be taken into account
Solution Approach 1:
The patent creates a universal planning framework where discrete acquisition opportunities are evaluated against a comprehensive set of constraints including local constraints, cumulative constraints, and specific requirements for stereo, tri-stereo, and multispectral acquisitions. This multi-functional approach allows the same system to handle diverse acquisition types without requiring separate specialized methods.
Data Source
AI summary
A method for planning the acquisition of images of areas Z1, . . . , ZN of the Earth, by a spacecraft on a mission around the Earth. Each area Zi of the Earth being associated with a request Ri such that a visual accessibility time interval Ti corresponds to the area Zi. For each interval Ti, discrete acquisition opportunities for acquiring area Zi are determined such that a start date, a period of execution, a local kinematic constraint and a weight is associated with each of the discrete acquisition opportunities. The discrete acquisition opportunities for acquiring areas Zi are grouped into a set D. The discrete acquisition opportunities Di of the set D are categorized by ascending start date. The kinematic compatibility between the discrete acquisition opportunities of set D is assessed. An optimal sequence of discrete acquisition opportunities is determined having a maximum weight, and being kinematically compatible.