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A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative
Yangyang Liu1, Pengyang Zhang1, Yu Ru2
1School of Engineering, Anhui Agricultural University, Hefei, China.
Frontiers in Plant Science
|October 3, 2022
Summary
This study introduces a novel algorithm for unmanned aerial vehicle (UAV) plant protection route planning in complex hilly terrains. The dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) significantly reduces flight range and search time for multi-tea field operations.
Area of Science:
- Agricultural Engineering
- Robotics
- Operations Research
Background:
- Hilly and mountainous regions present significant challenges for effective plant protection operations due to complex terrain and weak infrastructure.
- Existing route planning methods are often inefficient for large-scale agricultural tasks like tea cultivation in sloped areas.
Purpose of the Study:
- To develop an optimized unmanned aerial vehicle (UAV) route planning algorithm for multi-tea field plant protection in complex hilly environments.
- To enhance the efficiency and reduce the operational costs of agricultural spraying in challenging terrains.
Main Methods:
- Development of a dynamic genetic algorithm (DGA) with adaptive crossover and mutation probabilities.
- Proposal of an ant colony binary iteration optimization (ACBIO) incorporating iteration period and reinforcement concepts.
- Serial fusion of DGA and ACBIO (DGA-ACBIO) for multi-regional route planning.
Main Results:
- The DGA-ACBIO algorithm demonstrated a significant reduction in optimal flight range compared to existing algorithms (e.g., GA, ACO, AFSA, PSO).
- Search time was reduced by over 50% compared to other bionic algorithms.
- The algorithm showed superior performance, stability, planning accuracy, and search speed for complex route planning problems.
Conclusions:
- The DGA-ACBIO algorithm effectively addresses the challenges of plant protection route planning in complex hilly tea fields.
- This approach leads to reduced inter-regional scheduling distances and lower overall plant protection costs.
- The developed algorithm offers a robust solution for optimizing UAV operations in challenging agricultural landscapes.
