Cooperatively Routing a Truck and Multiple Drones for Target Surveillance

Area of Science:

• Robotics and Automation
• Operations Research
• Artificial Intelligence

Background:

• Drone technology is increasingly used for target surveillance in civilian and military applications.
• Limited drone endurance poses challenges for large-scale surveillance missions.
• Efficient route planning is crucial for cost-effective and extensive surveillance operations.

Purpose of the Study:

• To propose a new target surveillance mode using truck-drone cooperation to extend surveillance range.
• To optimize the routes of trucks and drones to minimize total operational cost.
• To address the challenges of large-scale target surveillance with limited drone endurance.

Main Methods:

• A cooperative surveillance model involving a truck carrying multiple drones was developed.
• A novel heuristic method, adaptive simulated annealing with large-scale neighborhoods, was created for route optimization.
• A scoring strategy was designed to dynamically adjust operator weights in the heuristic algorithm, considering drone energy consumption.

Main Results:

• The proposed truck-drone cooperation model effectively enlarges the surveillance range.
• The adaptive simulated annealing algorithm successfully optimized truck and drone routes for large-scale target surveillance.
• Extensive experiments on synthetic and real-world cases validated the algorithm's effectiveness.

Conclusions:

• The cooperative truck-drone surveillance mode offers innovative solutions for extending operational range and efficiency.
• The developed heuristic algorithm provides an effective method for solving complex, large-scale target surveillance routing problems.
• This research contributes new insights into optimizing autonomous systems for surveillance applications.