Vision-Based Spacecraft Navigation With Orbital Motion Constraints
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Solution Overview
Problem
Current navigation techniques for small celestial body missions lack autonomy, leading to high operational complexity and reliance on ground-based systems, which are inefficient due to long communication delays and limited bandwidth, making it difficult to achieve precise navigation and mission objectives.
Innovation Solution
A standalone vision-based system for autonomous online navigation using a factor graph formulation with sensor fusion and orbital motion constraints, incorporating a relative dynamics factor to predict spacecraft trajectories around small celestial bodies, utilizing an on-board camera and inertial equations to determine navigation trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground-in-the-loop navigation techniques are used, then navigation updates can be performed, but operational complexity increases and communication bandwidth is heavily utilized
Solution Approach 1:
The spacecraft performs autonomous navigation using on-board computer vision and SLAM algorithms, enabling the system to navigate and map the asteroid without continuous ground intervention. The navigation system processes images and computes trajectory updates independently, reducing operational complexity and communication bandwidth requirements while maintaining navigation accuracy
Solution Approach 2:
The navigation computation is extracted from the ground segment and transferred to the spacecraft's on-board computer. This removes the dependency on ground-based processing and communication links, allowing the spacecraft to autonomously perform trajectory determination and navigation updates using its own computational resources
2Measurement precision
If detailed topographic maps are constructed from image and LiDAR data, then shape reconstruction accuracy improves, but data collection time and ground processing effort increase
Solution Approach 1:
The SLAM system performs preliminary mapping and feature extraction continuously during the approach phase, building the topographic map incrementally as the spacecraft approaches the asteroid. This preliminary action enables shape reconstruction to begin earlier and with higher accuracy, reducing the total data collection time required
Solution Approach 2:
The navigation camera continuously captures images and the SLAM algorithm continuously processes these images to update the topographic map throughout the approach phase. This continuous mapping process ensures that shape reconstruction accuracy improves progressively without requiring separate dedicated mapping campaigns, reducing overall mission time
3Measurement precision
If frequent navigation updates are performed, then navigation precision improves, but communication bandwidth and ground assets are heavily utilized
Solution Approach 1:
The spacecraft autonomously performs navigation updates using on-board computer vision and SLAM algorithms, eliminating the need for frequent ground-based navigation updates. The system processes images and computes trajectory corrections independently, maintaining high navigation precision while minimizing communication bandwidth usage to only essential telemetry and command data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise and autonomous navigation around small celestial bodies, reducing operational complexity and improving navigation performance by enabling on-board estimation and path planning without ground-in-the-loop verification.
Implementation Method 1
using an on-board camera of the vehicle
Implementation Method 2
inertial equations of motion of the vehicle and of the small celestial body in conjunction with solar gravitational force
Implementation Method 3
solar radiation pressure forces
Data Source
AI summary
An exemplary system and method are disclosed for a standalone vision-based system for autonomous online navigation, e.g., around an unknown target small body. The exemplary system (also referred to as AstroSLAM) is predicated on the formulation of the SLAM problem as an incrementally growing factor graph. Using the GTSAM library and the iSAM2 engine, the exemplary system and method combine sensor fusion with the prior orbital motion to provide navigation and parameter estimation that improves the performance over a baseline SLAM implementation. The exemplary system and method can incorporate orbital motion constraints into the factor graph by devising a relative dynamics factor that can link the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body.