Search

Recently, with the continuous increase in the number of sensors, motors, actuators, radars, data processors and other components carried by humanoid robots, the integration of electronic components within a humanoid is also facing new challenges. Therefore, we focus on the development of sensor networks suitable for humanoid robots to designing an in-robot network (IRN) that can support a large sensor network for reliable data exchange. It was shown that the domain based in-vehicle network (IVN) architectures (DIA) used in the traditional and electric vehicles is gradually moving towards zonal IVN architectures (ZIA). Compared with DIA, ZIA for vehicles is known to provide better network scalability, maintenance convenience, shorter harness length, lighter harness weight, lower data transmission delay, and other several advantages. This paper introduces the structural differences between ZIRA and the domain based IRN architecture (DIRA) for humanoids. Additionally, it compares the differences in the length and weight of wiring harnesses of the two architectures. The results show that as the number of electrical components including sensors increases, ZIRA reduces at least 16% compared to DIRA, the wiring harness length, weight, and its cost. [ABSTRACT FROM AUTHOR]

This paper presents an information-theoretic approach to sensor placement that incorporates communication capacity into an optimal formulation. A new formulation is presented that maximizes the information rate achievable by a set of sensors communicating wirelessly to a single collection node. Shannon capacity and the standard radio propagation model are used to model the throughput achievable by a sensor configuration. Likewise, the d-optimality criterion from the active sensing literature is used to model information gain provided by range and bearing sensors. The combination of information-theoretic measures leads to a metric equivalent to the expected information rate achievable by the system. Sensor positions are selected that optimize this measure. [ABSTRACT FROM AUTHOR]