Coverage path planning for spraying drones

Area of Science:

• Robotics
• Urban Planning
• Environmental Science

Background:

• The COVID-19 pandemic highlights the need for effective public area disinfection.
• Automated disinfection solutions are crucial for future smart cities.
• Existing drone path planning methods are not optimized for spray coverage.

Purpose of the Study:

• To develop an advanced coverage path planning method for spraying drones.
• To address limitations of current methods regarding spray dispersion and flight boundaries.
• To enhance the efficiency and effectiveness of automated disinfection operations.

Main Methods:

• A new sprayer/sprinkler model was developed to realistically represent liquid dispersion.
• A path planning algorithm was designed to confine drone flight within the target region.
• The algorithm was tested in simulated environments to evaluate performance.

Main Results:

• The proposed method demonstrated superior area coverage compared to existing approaches.
• The sprayer model accurately reflects real-world liquid dispersion patterns.
• The planning method effectively prevents collisions in confined spaces.

Conclusions:

• The developed path planning method significantly improves drone-based disinfection efficiency.
• This technology can be applied to various applications beyond disinfection, such as painting and precision agriculture.
• The research contributes to the advancement of autonomous systems for urban sanitation and other industries.