RI: Small: RUI: Fission-Fusion Multi-Robot Systems

在现实世界中使用机器人团队的能力，如探索，侦察和搜索和救援，取决于他们在复杂和动态环境中有效合作的能力。不幸的是，确定一个给定的任务最有效的团队规模取决于各种因素，往往需要的信息不容易获得，并且可以计算是不切实际的。 来自自然界的观察表明，一些动物社会表现出频繁的分裂（裂变）和合并（融合）的子群，而没有协调开销经常发现在多机器人系统。 本研究课题的目的是利用自然界中的裂变-融合社会的这些见解作为灵感，在多机器人系统中实现类似的行为。本研究课题将调查促使机器人团队分裂成更小的群体，以及将更小的群体融合成更大的群体，并形成适合特定任务的规模的动机和机制。 潜在的生物和环境因素，有助于个人的决定，导致裂变和融合的群体，并有人工类似物相关的多机器人系统将被确定使用基于代理的模拟。 重点将放在有助于适应特定任务的首选组大小的因素。 一旦确定，这些因素将在物理机器人上实现，以评估决策系统在物理机器人约束条件下的性能。 所使用的决策系统将使用自适应模糊行为层次结构和集体决策模型，利用自然系统的观察。

The ability to use teams of robots in real-world tasks, such as exploration, reconnaissance and search and rescue, depends on their ability to effectively cooperate in complex and dynamic environments. Unfortunately, determining the most effective team size for a given task depends on a variety of factors, often requires information not readily available, and can be computationally impractical. Observations from nature show that some animal societies exhibit frequent splits (fission) and merges (fusion) of subgroups without the coordination overhead frequently found in multi-robot systems. The goal of this research project is to use these insights from fission-fusion societies in nature as inspiration to implement similar behavior in multi-robot systems.This project will investigate the motivations and mechanisms that contribute to the fission of a team of robots into smaller groups and the fusion of smaller groups into a larger group, into sizes appropriate to a given task. Potential biological and environmental factors that contribute to individual decisions that result in the fission and fusion of groups and that have artificial analogues relevant to multi-robot systems will be identified using agent-based simulations. An emphasis will be placed on factors that contribute to an adaptive task-specific preferred group size. Once identified, these factors will be implemented on physical robots to evaluate the performance of the decision-making system within the constraints of physical robots. The decision-making system used will be implemented using an adaptive fuzzy behavior hierarchy and a collective decision-making model developed using observations of natural systems.