An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems

Area of Science:

• Robotics and Automation
• Operations Research
• Artificial Intelligence

Background:

• Mission planning is crucial for multi-unmanned aerial vehicle (UAV) operations in military and civil sectors.
• Complex missions necessitate heterogeneous cooperative UAVs, requiring integrated sensor and path planning.
• Existing methods often address sensor allocation and path planning separately, limiting overall mission efficiency.

Purpose of the Study:

• To develop a mathematical model for integrated airborne sensor allocation and path planning for heterogeneous multi-UAV systems.
• To maximize total task profit while simultaneously minimizing travel costs.
• To present an efficient and effective mission planning framework.

Main Methods:

• Established a mathematical model for the integrated sensor allocation and path planning problem.
• Developed a two-level adaptive variable neighborhood search algorithm.
• Incorporated an adaptive mechanism to guide the search process intelligently.

Main Results:

• The proposed framework effectively integrates sensor allocation and path planning.
• Simulation results demonstrate the framework's superior performance compared to conventional methods.
• The adaptive mechanism enhanced the efficiency of the mission planning process.

Conclusions:

• The integrated mission planning framework significantly improves mission profit and reduces travel costs for heterogeneous multi-UAV systems.
• The two-level adaptive variable neighborhood search algorithm provides an effective solution for coupled planning problems.
• This research offers a more efficient and effective approach to complex UAV mission planning.