In this paper, a control architecture called Robotic Hybrid Indoor-Zone Operational ModulE (RHIZOME) is proposed as a new control paradigm capable of easy adaptation to different scenarios where a robot is able to interact with its environment and other cognitive agents while coping with possible unexpected situations. It creates a synergy of different state-of-the-art control paradigms by merging them into a neural structure, which follows a perception-action mechanism that constantly evolves because of the dynamic interaction of the robot with its environment. The RHIZOME architecture was tested on the NAO robot humanoid in an indoor vision-based navigation context. The proposed architecture was conceived, built and implemented through three different scenarios under which, three interdependent architectures emerged, each responding to different scenario constraints (deterministic and stochastic). Thanks to the generic composition, it is possible to develop it further with respect to robustness and completeness by simply adding new modules without modifying the already in-built components. Hence, it can be extended to perform other cognitive tasks. Experimental results obtained from its physical implementation show the feasibility, genericity and adaptability of the architecture.

中文翻译：

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根茎体系结构：认知移动机器人的自适应神经行为控制体系结构—在基于视觉的室内机器人导航环境中的应用

在本文中，提出了一种称为机器人混合室内区域操作模块（RHIZOME）的控制体系结构，它是一种新的控制范式，能够轻松适应机器人能够与其环境和其他认知主体互动同时应对可能的各种情况。意外情况。它通过将不同的最新控制范型合并到神经结构中来产生协同作用，该神经范结构遵循一种感知动作机制，该机制由于机器人与其环境之间的动态交互而不断发展。在基于室内视觉的导航环境中，在NAO机器人人形机器人上测试了RHIZOME体系结构。所提出的架构是通过三种不同的方案进行构思，构建和实施的，在这种情况下，出现了三种相互依赖的架构，每个响应于不同的场景约束（确定性和随机性）。由于具有通用的组成，因此可以通过简单地添加新模块而无需修改已经内置的组件来进一步提高其健壮性和完整性。因此，它可以扩展为执行其他认知任务。从其物理实现获得的实验结果表明了该体系结构的可行性，通用性和适应性。