Evolving Multi-Objective Neural Network Controllers for Robot Swarms
This addresses swarm robotics control for researchers, though it appears incremental as it applies existing evolutionary methods to a specific domain.
The researchers tackled the problem of controlling robot swarms with multiple conflicting objectives by proposing a multi-objective evolutionary neural network approach, demonstrating that controllers evolved in a low-fidelity simulator can be transferred to high-fidelity environments and scale to larger swarms without retraining.
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.