In-Robot Network Architectures for Humanoid Robots With Human Sensor and Motor Functions

The design concepts of the in-robot network (IRN) architectures of humanoid robots are proposed in this paper. First, this paper reveals the network requirements for humanoid robots to realize perception abilities and action execution abilities near those of human beings. Humanoid robots need to be equipped with many sensors to collect surrounding environmental information. It is also necessary to use many motion actuators to enhance the degree of freedom to achieve the smooth motion abilities of humans. To maintain reliable data transmission between a number of nodes, an efficient and reliable IRN architecture is needed. This paper first discusses the limitations of existing humanoid robots in the number of sensors and degrees of freedom and points out that one of the reasons for this limitation is the lack of reliable network architectures. In-vehicle networks (IVNs) and various network technologies are used. These include time-sensitive networks and control area networks (CANs) to name a few. Additionally, heterogeneous network protocols are used. IRNs also include networks of sensors and actuators with different performance parameters such as bandwidth, delay, and transmission speed. IRNs may adopt design ideas similar to those of IVNs to satisfy the network requirements for humanoids. To accomplish this, three feasible IRN architectures are proposed and analyzed. Finally, to compare and analyze the three proposed IRN architectures, this paper uses OMNeT++ simulation software.

Automated summary

This paper proposes the design concepts of IRN architectures for humanoid robots and discusses the network requirements to achieve perception abilities and action execution abilities. By analyzing the limitations of existing robots and using various network technologies and protocols, three feasible IRN architectures are proposed.