Cooperative pursuit-evasion control

Pursuit-evasion scenarios are a prevalent problem in the real-world, for example, autonomous search and rescue, aerospace defence and remote surveillance.

A pursuit-evasion problem is considered in which a team of slower, less agile pursuers must collaborate to capture a highly agile, high speed evader. The current leading approach only captures evaders exhibiting specific behaviours. 

Deep reinforcement learning is an emerging area for multi-agent control which has demonstrated unprecedented success in complex multi-agent tasks such as games like Starcraft 2 and Capture the Flag. Using a custom deep reinforcement learning architecture, we demonstrate that this new technique consistently outperforms the benchmark. The cooperative teaming methods were loaded into hardware, using a team of drones to demonstrate their effectiveness. A first of its kind drone control system was used, which improves the response between the autonomous commands and drone behaviour.

Theme
Transforming technologies

Booth
TT11

School
Mechanical Engineering

Exhibitors
Aaron Dadgar
Rhett Hull
Taylor Simpson

vote for this project: TT11

Back to project list