A Novel Simulated Annealing Based Strategy for Balanced UAV Task Assignment and Path Planning.

Area of Science:

• Robotics and Automation
• Operations Research
• Artificial Intelligence

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly vital for disaster response and military operations.
• Effective multi-UAV task assignment and path planning are crucial but challenging.
• Balancing workloads and optimizing solutions in large-scale problems remains difficult.

Purpose of the Study:

• To propose an efficient approach for multi-UAV task assignment and path planning.
• To balance tasks among UAVs and achieve satisfactory temporal resolutions.
• To enhance the efficiency of generating feasible solutions for complex optimization problems.

Main Methods:

• Modified Vehicle Routing Problem (VRP) model by adding virtual nodes.
• Introduced a universal distance matrix to convert temporal to spatial constraints.
• Developed a Swap-and-Judge Simulated Annealing (SJSA) algorithm for efficient solution generation.

Main Results:

• The proposed SJSA algorithm demonstrates efficiency in task assignment and path planning.
• Comparative studies show superior performance against exact and meta-heuristic algorithms.
• The approach effectively balances tasks and optimizes temporal resolutions for UAVs.

Conclusions:

• The proposed method offers an efficient solution for multi-UAV task assignment and path planning.
• The SJSA algorithm improves solution generation efficiency in constrained spaces.
• Findings highlight the potential of population-based algorithms for discrete optimization problems.