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.

翻译：许多群体机器人任务包含多个相互冲突的目标。本研究提出了一种多目标进化神经网络方法，用于开发群体机器人控制器。群体机器人控制器在低保真度的Python模拟器中进行训练，随后在采用Webots的高保真度模拟环境中进行测试。接着进行仿真实验，以检验进化所得的多目标机器人控制器在包含更多机器人的环境中的可扩展性。结果表明，所提出的方法能有效控制每个机器人。随着各目标权重的调整，机器人群体会展现出不同行为。研究结果还证实，在低保真度模拟器中进化的多目标神经网络控制器能够迁移至保真度更高的模拟环境，且无需额外训练即可扩展至包含更多机器人的环境。