Connected UAV Formation Control Using IMU-Based Parameter Estimation
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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, requiring quick and automatic identification of system dynamics without human intervention, and are limited by the need for expensive and impractical indoor positioning systems.
Innovation Solution
A method that determines control parameters for UAV formations by acquiring inertial data from Inertial Measurement Units (IMUs) and visual data, estimating connection parameters, and adapting control algorithms using machine learning models, allowing UAVs to automatically adjust their flight control based on estimated parameters without external positioning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control algorithms are adapted for each different formation configuration, then flight control reliability is improved, but system complexity and difficulty of automatic adaptation increase
Solution Approach 1:
The system pre-identifies and stores control parameters for multiple possible formation configurations before flight. When UAVs connect to form a formation, the system quickly matches the observed configuration to pre-stored parameters, avoiding real-time complex calculations and enabling rapid adaptation for reliable flight control.
Solution Approach 2:
The control system automatically identifies the formation configuration and selects appropriate control parameters without human intervention. The system self-adapts by comparing observed UAV connection parameters with stored configuration patterns, enabling autonomous control algorithm adaptation that improves reliability while reducing operational complexity.
2Measurement precision
If indoor positioning systems are used to estimate formation configuration, then measurement precision is improved, but cost and practicality deteriorate
Solution Approach 1:
The system replaces expensive, complex indoor positioning infrastructure with inexpensive IMU sensors already present on each UAV. By using low-cost inertial measurement units to track relative positions and orientations, the system achieves sufficient measurement precision for formation control without requiring costly motion capture systems or specialized positioning infrastructure.
Solution Approach 2:
The invention substitutes mechanical/optical positioning systems with inertial sensing-based virtual positioning. Instead of using physical markers, cameras, or radio frequency positioning infrastructure, the system uses IMU data processing to estimate formation configuration, replacing complex external positioning hardware with simpler onboard inertial sensors and computational algorithms.
3Productivity
If control parameters are determined quickly without human intervention, then productivity is improved, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system pre-computes and stores control parameters for various formation configurations before flight operations. During actual flight, the system only needs to identify the current configuration and retrieve pre-calculated parameters, enabling rapid parameter determination that maintains both speed and precision without requiring complex real-time calculations or human intervention.
Data Source
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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.