Robot

Algorithm

Process (computing)

Engineering

Computational complexity theory

Aerospace engineering

Biology

Motion planning

Control engineering

Drone

Real-time computing

Systems engineering

Stability (learning theory)

Operating system

Computation

Iterative and incremental development

Range (aeronautics)

Computer science

Distributed computing

Computer network

Genetics

Task (project management)

Path (computing)

Artificial intelligence

Software engineering

Machine learning

Aeronautics

Aerospace Science and Technology

In this paper, we propose an iterative strategy to enhance the performance of task assignment and path planning in applications of distributed multiple unmanned aerial vehicles (Multi-UAV). As an improvement of the conventional management of airborne computation and communication resources of UAVs, our strategy overcomes difficulties caused by the information coupling between task assignment and path planning. A distributed mission planning framework is presented with the strategy, in which the UAVs re-evaluate unreasonable assignment results and overvalued tasks during the planning process. The proposed strategy has advantages in algorithm stability and complexity, as it controls the task valuation error within a certain range via computation with limited complexity. Compared to the conventional methods, our strategy with the framework can achieve better performance of planning results and consume less computing resources. Simulation results show the effectiveness of the proposed strategy in terms of the computational efficiency and the mission execution reward.

An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles