Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior

Area of Science:

• Agricultural Engineering
• Robotics
• Artificial Intelligence

Background:

• Plant protection in mountainous regions presents unique challenges due to complex terrain and fragmented work plots.
• Traditional swarm intelligence algorithms for Unmanned Aerial Vehicle (UAV) path planning suffer from poor optimization, long operation times, and excessive iterations.

Purpose of the Study:

• To develop a novel bionic three-dimensional path planning algorithm for plant protection UAVs.
• To address the limitations of existing algorithms in terms of path optimization, operational efficiency, and convergence.

Main Methods:

• A bionic three-dimensional path planning algorithm inspired by krill swarm behavior, incorporating "foraging," "avoiding enemy," and "cruising" states.
• Utilizing UAV position information to adaptively switch between states for global and local convergence.
• Smoothing the optimal flight path using a third-order B-spline curve.

Main Results:

• The proposed algorithm reduced path length by 1.1-17.5% and operation time by 27.56-75.15%.
• Path energy consumption decreased by 13.91-27.35%, and the number of iterations reduced by 46-75% compared to existing methods.
• Demonstrated improved convergence speed and accuracy in path planning.

Conclusions:

• The bionic krill swarm algorithm offers a superior solution for plant protection UAV path planning in complex terrains.
• The algorithm enhances real-time performance and reduces energy consumption, making it highly effective for practical applications.