Connected UAV Formation Control Using Lift-Off IMU Identification
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Solution Overview
Problem
Existing systems for controlling formations of Unmanned Aerial Vehicles (UAVs) face challenges in adapting control algorithms to changing system dynamics when UAVs are connected in formations, requiring quick and automatic identification of system dynamics without human intervention, especially in outdoor applications where precise positioning systems are impractical.
Innovation Solution
A method that involves each UAV performing a lift-off procedure to acquire inertial data, estimating connection parameters such as relative orientation and distance, and determining control parameters using filtered data from Inertial Measurement Units (IMUs) and visual sensors, allowing for automatic adaptation of control algorithms without external positioning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If control algorithms are adapted for each different formation configuration, then flight stability and control precision are improved, but system complexity and difficulty of implementation increase
Solution Approach 1:
The system performs self-identification of formation configuration by automatically detecting relative positions and orientations of UAVs through IMU data and lift-off procedures, eliminating the need for external operators to manually configure or specify the formation geometry. The control parameters are automatically determined based on the detected configuration, allowing the system to adapt itself without external intervention.
Solution Approach 2:
The system dynamically determines control parameters based on the detected formation configuration parameters (relative positions, orientations, and connections between UAVs). By changing control parameters according to the identified configuration, the system adapts to different formation types (e.g., linear, triangular, rectangular arrangements) without requiring pre-programmed knowledge of each specific configuration.
2Adaptability or versatility
If automatic identification of system dynamics is implemented, then adaptability to different formations is improved, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary identification of formation configuration through controlled lift-off procedures before actual flight operations. By detecting relative positions and orientations during the lift-off phase when UAVs are transitioning from ground to air, the system establishes the formation configuration in advance, allowing subsequent flight control to proceed without time-consuming real-time analysis during critical flight phases.
Solution Approach 2:
The system rapidly determines formation configuration by focusing measurements and computations on critical parameters during the brief lift-off transition period. Rather than continuously analyzing all possible motion parameters during entire flight sequences, the system concentrates processing on identifying relative positions and orientations during the specific lift-off phase, thereby reducing overall processing time while maintaining accuracy.
3Measurement precision
If expensive precise positioning systems are used, then formation estimation accuracy is improved, but cost and practicality for outdoor applications worsen
Solution Approach 1:
The system replaces expensive mechanical/optical positioning systems (such as motion capture systems with cameras and markers) with inertial measurement units (IMUs) that use accelerometers and gyroscopes to detect motion and position. This substitution enables outdoor applications where optical systems would fail due to lighting conditions, occlusions, or lack of controlled environment, while maintaining sufficient accuracy for formation flight control.
Solution Approach 2:
The system uses the ground as an intermediary reference surface during the lift-off procedure. By detecting the transition from ground contact to air flight and analyzing motion characteristics relative to the ground, the system can accurately determine relative positions and orientations of UAVs without requiring external positioning infrastructure. The ground serves as a natural reference that eliminates the need for expensive artificial positioning systems.
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 real-time determination of control parameters for UAV formations, enabling them to lift off and fly stably without pre-defined configurations, improving payload and flight time capabilities while eliminating the need for expensive indoor positioning systems.
Implementation Method 1
acquiring, for each lift off procedure, inertial data from Inertial Measurement Units, IMUs, of each one of the UAVs of the formation
Data Source
AI summary
According to a first aspect, it is provided a method for determining control parameters for controlling flight of a formation comprising at least two physically connected UAVs. The method is performed in a controller device and comprising the steps of: determining UAVs forming part of the formation; controlling each one of the UAVs, in sequence, to perform a lift off procedure in which the UAV lifts off ground and lands on ground; acquiring, for each lift off procedure, inertial data from Inertial Measurement Units, IMUs, of each one of the UAVs of the formation; estimating UAV connection parameters for each possible pair of UAVs of the formation based on the inertial data, the UAV connection parameter comprising at least one of relative orientation, absolute orientations and distance between the UAVs of the pair; and determining control parameters for controlling flight of the formation based on the estimated connection parameters.