UAV Route Planning by Sub-Area Sequencing for Accurate Spraying
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Solution Overview
Problem
Current UAV route planning methods are heavily influenced by human factors, leading to inaccuracies due to fatigue and visual errors, resulting in route deviations and reduced operational accuracy.
Innovation Solution
A method that divides the UAV operation area into sub-areas, exhausts operation orders and waypoint sequences, and determines a globally optimal route by sorting and pairing these sequences to meet preset constraint conditions, such as the shortest voyage, thereby improving accuracy and reducing redundant travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual control or remote control is used for UAV route planning, then the system is simple and easy to operate, but human factors such as fatigue and visual errors cause route deviation and reduce accuracy
Solution Approach 1:
The patent replaces manual visual control and remote control systems with an automated computer-based route planning system. The computer automatically calculates and generates optimal routes using algorithms, eliminating human factors such as fatigue and visual errors that cause route deviations. This substitution of mechanical/manual systems with automated computational systems directly improves route planning accuracy while accepting increased system complexity.
2Measurement precision
If automated route planning is implemented to improve accuracy, then route planning precision improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent divides the operation area into multiple sub-areas and processes route planning for each sub-area separately. By segmenting the overall route planning problem into smaller, manageable sub-problems, the system can calculate optimal paths for each sub-area independently and then integrate them. This segmentation reduces the computational complexity of the overall system while maintaining high route planning accuracy through systematic processing of each segment.
3Productivity
If exhaustive search of all possible routes is performed to find the globally optimal route, then the route optimization improves, but the computational time and resources increase
Solution Approach 1:
The patent segments the operation area into sub-areas and performs route planning for each sub-area separately rather than calculating all possible routes for the entire area at once. This segmentation allows the system to exhaustively search for optimal routes within each smaller sub-area (ensuring global optimality within constraints) while significantly reducing the total computational time compared to searching the entire operation area as a single large space.
Solution Approach 2:
The patent performs preliminary processing by dividing the operation area into sub-areas and pre-processing waypoint sequences for each sub-area before conducting the exhaustive route search. This preliminary action organizes the data structure and reduces the search space, allowing the subsequent exhaustive search to be more efficient while still guaranteeing finding the globally optimal route that meets preset constraint conditions.
Data Source
AI summary
The present disclosure provides a UAV operation route planning method, a UAV pesticide spreading planning method and device for providing improvements on the operation accuracy of UAV. The UAV operation route method comprises steps of: obtaining a plurality of sub-areas of an operation area of a UAV; exhausting operation orders of the sub-areas and waypoint sequences in each of the sub-areas, respectively; planning routes according to the operation orders of the sub-areas and the waypoint sequences in each of the sub-areas to obtain all routes in the operation area; and determining a route in all the routes having a total voyage meeting a preset constraint condition as an optimal operation route.