AI Satellite Panel Steering for Autonomous Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing number of satellites in orbit has led to a higher probability of collisions, posing a significant risk for technical and financial loss, necessitating effective satellite collision avoidance technology that integrates aerospace, computer vision, and artificial intelligence.
Innovation Solution
A smart satellite device equipped with LIDAR sensors and a deep reinforcement learning system processes data to predict potential collisions, using convolutional neural networks and reinforcement learning agents to adjust satellite panels for optimal trajectory control and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of satellites in orbit is increased to expand space infrastructure, then satellite coverage and functionality are improved, but the probability of satellite collisions increases
Solution Approach 1:
The system performs preliminary collision risk assessment and trajectory optimization before collisions occur. The AI model continuously predicts potential collision scenarios and pre-calculates avoidance maneuvers, enabling satellites to take preventive actions before actual collision threats materialize, thus maintaining high satellite density while reducing collision probability
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where LIDAR sensors continuously monitor the orbital environment, feed real-time data to the AI collision avoidance model, which then adjusts satellite trajectories dynamically. This continuous feedback loop enables adaptive collision prevention in dense orbital environments, allowing increased satellite deployment while maintaining safety through real-time adjustments
2Reliability
If traditional satellite control systems are used, then system simplicity is maintained, but collision avoidance capability is insufficient
Solution Approach 1:
The satellite collision avoidance system operates autonomously using onboard LIDAR sensors and AI processing. The system self-monitors its orbital environment, self-assesses collision risks, and self-executes avoidance maneuvers without requiring ground control intervention, thereby achieving high collision avoidance capability while managing complexity through automation
Solution Approach 2:
The patent replaces traditional mechanical control systems with an AI-based intelligent system. Instead of relying on pre-programmed mechanical control sequences, the system uses machine learning models that process sensor data and generate control commands dynamically, substituting complex mechanical control architecture with flexible software-based intelligence
3Measurement precision
If LIDAR sensors and AI processing systems are added to satellites, then collision detection accuracy is improved, but satellite weight and power consumption increase
Solution Approach 1:
The system optimizes the operational parameters of LIDAR sensors to balance detection accuracy with weight and power constraints. By adjusting scanning frequencies, detection ranges, and processing intensities dynamically based on orbital conditions, the system maintains high collision detection accuracy while minimizing the weight and power overhead of the sensing and processing infrastructure
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
The system enhances satellite safety by autonomously identifying potential collisions and adjusting trajectories, reducing the risk and cost associated with satellite collisions.
Implementation Method 1
a LIDAR sensor searching and sensing a trajectory environment and signaling data
Data Source
AI summary
The present disclosure is a device and methods for satellite trajectory control and collision avoidance. Embodiments of the disclosure are comprised of a smart satellite device performing a process three steps. First, sensors collect data about the satellite's physical landing environment, passing information to satellite's database and processors. Second, the processors manipulate the information with a deep reinforcement learning program to produce instructions. Third, the instructions steer the satellite body by manipulating the satellite's panels for optimal trajectory and collision avoidance. The purpose for the present disclosure is to help solve the space debris problem.