Multi-UAV Coverage Path Planning with Q-Learning Ant Colony Adaptation
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Solution Overview
Problem
Existing path planning methods for unmanned aerial vehicles (UAVs) in cooperative coverage tasks are computationally intensive and complex, struggling with efficiency and robustness, especially in complex environments.
Innovation Solution
A multi-UAV cooperative coverage path planning method using an improved ant colony algorithm with a Q-learning adaptive strategy, involving a memory, image collection device, and UAVs with a path planning module that constructs a 3D model, performs cell division, establishes constraints, and iteratively optimizes paths using pheromone augmentation and adaptive heuristic adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing path planning methods are used for multi-UAV cooperative coverage tasks, then path planning can be performed, but computational complexity increases and efficiency decreases
Solution Approach 1:
The patent divides the continuous path planning problem into discrete grid cells, transforming the complex continuous optimization problem into a discrete search problem. The search space is segmented into manageable grid units that can be processed independently, reducing computational complexity while maintaining planning efficiency
Solution Approach 2:
The patent transforms the path planning problem by changing parameters from continuous coordinates to discrete grid cell indices. This parameter transformation simplifies the mathematical complexity of path planning while preserving the essential optimization objectives, enabling more efficient computation
2Use of energy by moving object
If existing path planning methods are used for multi-UAV cooperative coverage tasks, then coverage can be achieved, but energy consumption increases
Solution Approach 1:
The patent implements feedback mechanisms where the ant colony algorithm learns from previous path selections and iteratively improves solutions. The pheromone updates provide continuous feedback that guides UAVs toward energy-efficient paths while ensuring reliable coverage, balancing energy consumption with task completion reliability
Solution Approach 2:
The patent replaces traditional mechanical path planning approaches with a bio-inspired ant colony optimization system. This substitution enables more adaptive and energy-efficient path finding through collective intelligence, reducing overall energy consumption while maintaining robust coverage capabilities
3Speed
If traditional algorithms are used for path optimization, then solutions can be found, but convergence speed is slow
Solution Approach 1:
The patent employs dynamic parameter adjustment where the ant colony algorithm adapts its search behavior during execution. Pheromone evaporation rates and heuristic weights are dynamically adjusted to balance exploration and exploitation, enabling fast convergence while maintaining path optimization precision throughout the iterative process
Data Source
AI summary
A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.