Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Distributed Systems

Background:

• Persistent surveillance is an NP-hard problem for multiple unmanned aerial vehicle (UAV) systems.
• Existing research often overlooks dynamic deployment in multi-swarm UAV systems, focusing instead on single-swarm cooperative path planning.

Purpose of the Study:

• To propose a collective control scheme for multiswarm UAV systems enabling dynamic deployment.
• To provide persistent adaptive sensor coverage in time-sensitive, event-related surveillance scenarios.

Main Methods:

• Development of a digital turf model to integrate mission requirements and surveillance rewards.
• Implementation of a data clustering-based algorithm for dynamic UAV swarm assignment, ensuring workload balance and emergency response.
• Simulation-based evaluation of the proposed architecture.

Main Results:

• The proposed collective control scheme effectively drives multiswarm UAV systems for dynamic deployment.
• The data clustering algorithm promotes workload balance and real-time adaptability to emergencies.
• Simulations demonstrate superior performance compared to conventional strategies.

Conclusions:

• The developed collective control scheme offers an effective solution for multiswarm UAV persistent surveillance.
• The approach enhances detection efficiency and subswarm equilibrium in dynamic environments.
• This work advances the field of distributed UAV control for complex surveillance missions.