Evolving Multi-Objective Neural Network Controllers for Robot Swarms
Karl Mason Sabine Hauert
Many swarm robotics tasks consist of multiple conflicting objectives. This
research proposes a multi-objective evolutionary neural network approach to
developing controllers for swarms of robots. The swarm robot controllers are
trained in a low-fidelity Python simulator and then tested in a high-fidelity
simulated environment using Webots. Simulations are then conducted to test the
scalability of the evolved multi-objective robot controllers to environments
with a larger number of robots. The results presented demonstrate that the
proposed approach can effectively control each of the robots. The robot swarm
exhibits different behaviours as the weighting for each objective is adjusted.
The results also confirm that multi-objective neural network controllers
evolved in a low-fidelity simulator can be transferred to high-fidelity
simulated environments and that the controllers can scale to environments with
a larger number of robots without further retraining needed.