Multi-UAV Path Planning with Variable Speed and Wind Field Optimization
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Solution Overview
Problem
Current UAV path planning models assume constant speed, making it impossible to accurately simulate the actual state of motion and achieve optimal path planning, especially under varying environmental conditions like wind fields.
Innovation Solution
A method for joint optimization of multi-UAV task assignment and path planning that considers the dispersion of groundspeed course angles and wind fields, using a genetic algorithm to determine the flight status and time of each UAV, and iteratively refining the Dubins flight paths to find the shortest time solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If constant speed is assumed in the path planning model, then the model is simple to implement, but it cannot accurately simulate the actual state of motion of UAVs under varying environmental conditions
Solution Approach 1:
The patent transitions from a static constant-speed model to a dynamic variable-speed model that adapts to environmental conditions. The UAV speed is no longer fixed but varies according to wind field conditions, allowing the model to accurately reflect actual motion states while maintaining computational feasibility through structured algorithms.
Solution Approach 2:
The patent changes the speed parameter from a constant value to a variable that depends on environmental factors. By introducing wind field parameters and calculating groundspeed based on the vector sum of airspeed and wind speed, the model dynamically adjusts speed parameters to match real-world conditions, resolving the contradiction between model simplicity and accuracy.
2Measurement precision
If variable speed and wind field conditions are considered in the path planning model, then the accuracy of motion simulation is improved, but the computational complexity and difficulty of optimization increase
Solution Approach 1:
The patent segments the optimization problem into distinct components: task assignment and path planning. By using a two-stage approach where task assignment determines the sequence of target visits and path planning optimizes the trajectories, the complex variable-speed optimization is broken down into manageable sub-problems that can be solved more efficiently.
Solution Approach 2:
The patent performs preliminary task assignment before detailed path planning. By first determining which UAV visits which target and in what sequence, the subsequent path optimization operates on a fixed task structure, reducing the search space and computational complexity of the variable-speed path planning phase.
3Ease of operation
If traditional path planning models are used without considering groundspeed dispersion, then the planning process is straightforward, but the task completion time cannot be accurately calculated
Solution Approach 1:
The patent incorporates groundspeed dispersion feedback into the time calculation process. By considering the statistical distribution of groundspeeds rather than a single deterministic value, the model provides more accurate expected task completion times while maintaining operational simplicity through established statistical methods.
Data Source
AI summary
The embodiments of the present invention disclose a method and apparatus for joint optimization of multi-UAV task assignment and path planning. The method comprises: obtaining the location information of a plurality of UAVs and a plurality of target points, the dispersion of groundspeed course angle, and motion parameters of each UAV and wind field; constructing an initial population based on the location information, the dispersion of groundspeed course angle and a preset genetic algorithm; determining the flight status of each UAV and the flight time taken by each UAV to complete a path segment of the corresponding Dubins flight path based on the initial population and the motion parameters, obtaining the total time taken by all the UAVs corresponding to each chromosome to complete the task based on the flight time of the path segment; and subjecting the chromosomes in the initial population to crossover and mutation based on the genetic algorithm and, when a predetermined number of iterations is reached, selecting the optimal Dubins flight path as the joint optimization result. In the embodiments of the present invention, the UAV flight path planning problem is combined with the actual flight environment of the UAV, so that the optimal flight path obtained is superior to the solution in which the UAV speed is constant.