Swarm Intelligence
Swarm Intelligence
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DOI：
10.1007/978-3-319-44427-7
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发表时间：
2016
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本文描述了一系列实验，其中需要一组同类的真实e-Puck机器人来协调它们的动作，以便运送单个机器人无法移动的太重的长方体物体。智能体控制器是通过进化计算技术合成的动态神经网络。为了进行这些实验，我们设计、建造了一个新的传感器，并将其安装在机器人上，它可以返回x/y平面上的代理位移。在这个物体运输场景中，该传感器对机器人动作的结果产生有用的反馈，帮助机器人感知它们的推力是否与物体的运动对齐。实验结果表明，最优进化控制器能够有效地在真实机器人上运行。事实证明，群体传输策略是健壮的和可扩展的，可以在各种条件下有效地操作，在这些条件下，我们可以改变对象的物理特征和群体基数。从生物学的角度来看，这项研究的结果表明，对物体运动的感知可以解释自然生物如何协调它们的行动来运输重物。
This paper describes a set of experiments in which a homogeneous group of real e-puck robots is required to coordinate their actions in order to transport cuboid objects that are too heavy to be moved by single robots. The agents controllers are dynamic neural networks synthesised through evolutionary computation techniques. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agent displacement on the x/y plane. In this object transport scenario, this sensor generates useful feedback on the consequences of the robot actions, helping the robots to perceive whether their pushing forces are aligned with the object movement. The results of our experiments indicated that the best evolved controller can effectively operate on real robots. The group transport strategies turned out to be robust and scalable to effectively operate in a variety of conditions in which we vary physical characteristics of the object and group cardinality. From a biological perspective, the results of this study indicate that the perception of the object movement could explain how natural organisms manage to coordinate their actions to transport heavy items.