This paper discusses the design and interface of NUClear, a new hybrid messagepassing architecture for embodied humanoid robotics. NUClear is modular, has low latency, and promotes functional and expandable software design. It greatly reduces the latency for messages passed between modules as the message routes are established at compile time. It also reduces the number of functions that must be written using a system called co-messages, which aids in dealing with multiple simultaneous data. NUClear has primarily been evaluated on a humanoid robotic soccer platform and on a robotic boat platform. Evaluations show that NUClear requires fewer callbacks and cache variables over existing message-passing architectures. NUClear does have limitations when applying these techniques on multi-processed systems. It performs best in lower power systems where computational resources are limited. This article aims at readers with interest in modern software engineering concepts and development of systems in areas such as robotics, smart devices and virtual reality.
This paper proposes an enhancement of convolutional neural networks for object detection in resource-constrained robotics through a geometric input transformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrate execution times sixteen times quicker than the fastest competitor tested, while achieving outstanding accuracy.
The goal of this study is to test two different computing platforms with respect to their suitability for running deep networks as part of a humanoid robot software system. One of the platforms is the CPUcentered Intel R NUC7i7BNH and the other is a NVIDIA R Jetson TX2 system that puts more emphasis on GPU processing. The experiments addressed a number of benchmarking tasks including pedestrian detection using deep neural networks. Some of the results were unexpected but demonstrate that platforms exhibit both advantages and disadvantages when taking computational performance and electrical power requirements of such a system into account.
The NUbots are an interdisciplinary RoboCup team from The University of Newcastle, Australia. The team has a history of strong contributions in the areas of machine learning and computer vision. The NUbots have participated in RoboCup leagues since 2002, placing first several times in the past. In 2014 the NUbots also partnered with the University of Newcastle Mechatronics Laboratory to participate in the RobotX Marine Robotics Challenge, which resulted in several new ideas and improvements to the NUbots vision system for RoboCup. This paper summarizes the history of the NUbots team, describes the roles and research of the team members, gives an overview of the NUbots' robots, their software system, and several associated research projects.
The NUbots team, from The University of Newcastle, Australia, has had a strong record of success in the RoboCup Standard Platform League since first entering in 2002. The team has also competed within the RoboCup Humanoid Kid-Size League since 2012. The 2014 team brings a renewed focus on software architecture, modularity, and the ability to easily share code. This paper summarizes the history of the NUbots team, describes the roles and research of the team members, gives an overview of the NUbots' robots and software system, and addresses relevant research projects within the the Newcastle Robotics Laboratory.
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