UAV Tree Pruning Control Using Multi-View Shape Learning
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Solution Overview
Problem
Current unmanned aerial vehicle (UAV) systems lack the capability to accurately specify pruning positions on trees, particularly for various types of branches, which hinders efficient tree pruning and shape adjustment.
Innovation Solution
A control apparatus for UAVs that utilizes a shape generation neural network to generate accurate tree shape information from multiple images taken from different directions, allowing for precise specification of pruning positions and machine-learning-based control of the pruning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple tree images are captured from different directions to improve tree shape understanding, then the accuracy of pruning position specification is improved, but the time and complexity of image processing increases
Solution Approach 1:
The system performs preliminary actions by capturing multiple tree images from different directions before the actual pruning operation. These images are processed in advance to generate comprehensive tree shape information, which is then used to accurately specify pruning positions. This preliminary image capture and processing resolves the contradiction by preparing data beforehand, allowing accurate pruning position specification without time pressure during the actual pruning operation.
2Measurement precision
If a shape generation neural network is used to generate tree shape information, then the accuracy of tree shape understanding is improved, but the device complexity increases
Solution Approach 1:
The shape generation neural network acts as an intermediary between the captured tree images and the pruning position specification system. It processes the raw image data and generates structured tree shape information that is easier to interpret and use for determining pruning positions. This intermediary neural network layer resolves the contradiction by transforming complex image data into useful shape information, improving tree shape understanding accuracy while managing system complexity through specialized data transformation.
3Productivity
If autonomous control of UAV flight and pruning operation is implemented, then productivity is improved, but the difficulty of control increases
Solution Approach 1:
The system implements autonomous control by establishing a feedback loop where the generated tree shape information is continuously used to specify pruning positions, which then guide the UAV's flight and pruning operations. The system processes the tree shape data, determines optimal pruning positions, and automatically controls the UAV to execute the pruning. This feedback-based autonomous control resolves the contradiction by creating a self-regulating system that improves pruning operation efficiency while managing control complexity through automated decision-making based on the generated shape information.
Data Source
AI summary
Problems to be SolvedTo provide a control apparatus for an unmanned aerial vehicle and an unmanned aerial system capable of pruning a tree by appropriately specifying a pruning position to prune a branch of the tree and further adjusting a shape of the tree.[Solution]A control apparatus 3 for an unmanned aerial vehicle 2 according to the present invention includes a tree shape information generation section 312 capable of generating tree shape information of a target tree T1 targeted for pruning by using two or more tree images P of the target tree T1 taken from different directions and a shape generating neural network N1; a pruning position specifying section 316 capable of specifying a pruning position of the target tree T1 by using the tree shape information; an operation control section 318 capable of controlling a flight state of the unmanned aerial vehicle and an operation of the pruning structure in accordance with the pruning position P; a tree shape evaluation receiving section 313 capable of receiving a tree shape evaluation related to the tree shape information; and a shape learning section 314 capable of causing the shape generation neural network N1 to machine-learn a shape of the tree on the basis of the tree images, the tree shape information, and the tree shape evaluation.