Multi-UAV Formation Control for GPS-Denied Obstacle Passage
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Solution Overview
Problem
Existing methods for multi-UAV formations to pass through frame-shaped obstacles rely on GPS or cameras, which are ineffective in environments with limited GPS signals and poor lighting, leading to difficulties in positioning and navigation.
Innovation Solution
A method using on-board sensors, including UWB distance sensors and IMU, to construct relative position and velocity models, enabling the UAV formation to pass through obstacles without GPS or visual data, with low computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS modules and cameras are installed on UAVs for positioning and obstacle recognition, then positioning accuracy and obstacle identification capability are improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensing capabilities (UWB radio frequency positioning, infrared thermal imaging, and visible light camera) into an integrated sensor system. The UWB module provides accurate distance measurement through time-of-flight calculation, the infrared sensor captures thermal radiation patterns for obstacle detection, and the camera provides visual confirmation. These sensors work synergistically through data fusion algorithms to achieve comprehensive obstacle recognition and positioning without requiring separate complex systems for each function.
Solution Approach 2:
The sensor system is designed to perform multiple functions simultaneously: the UWB module serves both for inter-UAV distance measurement and for positioning relative to the obstacle; the infrared sensor detects both thermal signatures of obstacles and provides depth information; the camera captures visual data for navigation and documentation. This multi-functional design reduces the need for specialized hardware for each task, thereby reducing overall device complexity while maintaining high measurement precision.
2Reliability
If GPS and camera systems are used for navigation, then obstacle passage capability is improved, but reliability deteriorates in GPS-denied and low-light environments
Solution Approach 1:
The system employs different sensing modalities optimized for specific environmental conditions: the UWB module operates effectively in GPS-denied environments by using radio frequency time-of-flight measurement that is unaffected by satellite signal availability; the infrared sensor provides reliable obstacle detection in low-light conditions by detecting thermal radiation rather than visible light; the camera provides supplemental visual information when lighting conditions are adequate. This localized optimization of sensor performance ensures reliable navigation across diverse environmental conditions.
Solution Approach 2:
The navigation system uses a composite sensing approach, combining data from UWB radio frequency positioning, infrared thermal imaging, and visible light photography. Each sensor type contributes its unique capabilities: UWB provides accurate range measurement through electromagnetic time-of-flight, infrared detects thermal contrasts for obstacle identification in darkness, and the camera provides contextual visual information. The fusion of these heterogeneous data sources creates a robust navigation system that maintains reliability across varying environmental conditions where any single sensor type would fail.
3Productivity
If multiple sensors and complex algorithms are deployed for obstacle passage, then navigation capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary processing of sensor data to extract critical features before full navigation decision-making. The UWB module continuously calculates distance to the obstacle and updates position estimates in advance, allowing the navigation algorithm to plan trajectories proactively rather than reactively. The infrared and camera sensors pre-identify potential obstacles and their thermal/visual characteristics, enabling the control system to prepare appropriate avoidance maneuvers before collision risk becomes critical. This preliminary action reduces real-time computational burden during actual obstacle passage.
Solution Approach 2:
The sensor system and navigation algorithms are integrated into the UAV's autonomous operation, where the system serves itself by automatically processing sensor data, identifying obstacles, calculating safe trajectories, and executing navigation commands without external intervention. The embedded processor fuses data from UWB, infrared, and camera sensors to generate navigation decisions independently, reducing the need for complex external control systems or manual operation. This self-service capability streamlines the overall system architecture and reduces computational complexity by eliminating redundant processing layers.
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 adaptive obstacle passage in various environments with reduced hardware requirements and computational load, ensuring robust UAV formation navigation.
Implementation Method 1
obtain real-time distance and displacement data through the on-board sensor of the UAV and the UWB distance sensor
Implementation Method 2
obtain real-time distance and displacement data through the on-board sensor of the UAV and the UWB distance sensor
Data Source
AI summary
A method for a multi-UAV formation to pass through a frame-shaped obstacle, includes: obtaining in real time distance between the frame-shaped obstacle and the UAV formation and displacement of the UAV formation during a flight process; constructing a relative position model between the frame-shaped obstacle and the UAV formation; constructing a UAV velocity control model; constructing a parameter model based on the distance, the displacement, and the relative position model; constructing a position estimation model of the frame-shaped obstacle and a relative position estimation model among the UAVs based on the parameter model; and designing, based on the constructed position estimation model, the relative position estimation model, and the UAV velocity control model, a controller for the UAV formation to pass through the obstacle, and controlling a flight velocity of the UAV formation through the controller to enable the UAV formation to pass through the frame-shaped obstacle.