Applications of Intelligent Vision in Low-Cost Mobile Robots

With the development of intelligent information technology, we have entered an era of 5G and AI. Mobile robots embody both of these technologies, and as such play an important role in future developments. However, the development of perception vision in consumer-grade low-cost mobile robots is still in its infancies. With the popularity of edge computing technology in the future, high-performance vision perception algorithms are expected to be deployed on low-power edge computing chips. Within the context of low-cost mobile robotic solutions, a robot intelligent vision system is studied and developed in this thesis. The thesis proposes and designs the overall framework of the higher-level intelligent vision system. The core system includes automatic robot navigation and obstacle object detection. The core algorithm deployments are implemented through a low-power embedded platform. The thesis analyzes and investigates deep learning neural network algorithms for obstacle object detection in intelligent vision systems. By comparing a variety of open source object detection neural networks on high performance hardware platforms, combining the constraints of hardware platform, a suitable neural network algorithm is selected. The thesis combines the characteristics and constraints of the low-power hardware platform to further optimize the selected neural network. It introduces the minimize mean square error (MMSE) and the moving average minmax algorithms in the quantization process to reduce the accuracy loss of the quantized model. The results show that the optimized neural network achieves a 20-fold improvement in inference performance on the RK3399PRO hardware platform compared to the original network. The thesis concludes with the application of the above modules and systems to a higher-level intelligent vision system for a low-cost disinfection robot, and further optimization is done for the hardware platform. The test results show that while achieving the basic service functions, the robot can accurately identify the obstacles ahead and locate and navigate in real time, which greatly enhances the perception function of the low-cost mobile robot

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

Advancing automation and robotics technology for the Space Station and for the US economy. Volume 1: Executive overview

In response to Public Law 98-371, dated July 18, 1984, the NASA Advanced Technology Advisory Committee has studied automation and robotics for use in the Space Station. The Executive Overview, Volume 1 presents the major findings of the study and recommends to NASA principles for advancing automation and robotics technologies for the benefit of the Space Station and of the U.S. economy in general. As a result of its study, the Advanced Technology Advisory Committee believes that a key element of technology for the Space Station is extensive use of advanced general-purpose automation and robotics. These systems could provide the United States with important new methods of generating and exploiting space knowledge in commercial enterprises and thereby help preserve U.S. leadership in space

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing