Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Bringing Japanese manufacturing systems to America: the feasibility of implementing just-in-time production in the United States

This paper addresses the feasibility of implementing Japanese manufacturing systems in the United States. The recent success of Japanese transplant companies suggests that Just-In-Time (JIT) production is possible within America's industrial environment. Once American workers receive proper training, they have little difficulty participating in rapid setup procedures and utilizing the kanban system. Japanese transplants are gradually developing Japanese-style relationships with their American supplier companies by initiating long-term, mutually beneficial agreements. They are also finding ways to cope with America's problem of distance, which is steadily decreasing as an obstacle to JIT delivery. American companies, however, encounter Significant problems in trying to convert traditionally organized, factories to the JIT system. This paper demonstrates that it is both feasible and beneficial for American manufacturers to implement JIT production techniques. Many of the difficulties manufacturers experience center around a general lack of information about JIT. Once a company realizes its potential for setup-time reduction, a prerequisite for the JIT system, workers and managers can work together to create a new process for handling equipment changeover. Significant results are possible with minimal investment. Also, supervisors often do not realize that the JIT method of ordering goods from suppliers is compatible with current systems. This "kanban system" not only enhances current systems but also reduces the amount of paperwork and scheduling involved. When arranging JlT delivery of supplier goods, American manufacturers tend to overlook important aspects of JIT supplier management. However, by making long-tenn commitments, initiating the open exchange of information, assisting suppliers in reaching new standards of performance, increasing the level of conununication, and relying more on suppliers' engineering capabilities, even American manufacturers can develop Japanese-style supplier relationships that enhance the effectiveness of the system.