Human Motion Recognition through Fuzzy Hidden Markov Model

A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently

Survey: Robot Programming by Demonstration

Robot PbD started about 30 years ago, growing importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training the robot to perform a task is three-fold. First and foremost, PbD, also referred to as {\em imitation learning} is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution, by either starting the search from the observed good solution (local optima), or conversely, by eliminating from the search space what is known as a bad solution. Imitation learning is, thus, a powerful tool for enhancing and accelerating learning in both animals and artifacts. Second, imitation learning offers an implicit means of training a machine, such that explicit and tedious programming of a task by a human user can be minimized or eliminated (Figure \ref{fig:what-how}). Imitation learning is thus a ``natural'' means of interacting with a machine that would be accessible to lay people. And third, studying and modeling the coupling of perception and action, which is at the core of imitation learning, helps us to understand the mechanisms by which the self-organization of perception and action could arise during development. The reciprocal interaction of perception and action could explain how competence in motor control can be grounded in rich structure of perceptual variables, and vice versa, how the processes of perception can develop as means to create successful actions. PbD promises were thus multiple. On the one hand, one hoped that it would make the learning faster, in contrast to tedious reinforcement learning methods or trials-and-error learning. On the other hand, one expected that the methods, being user-friendly, would enhance the application of robots in human daily environments. Recent progresses in the field, which we review in this chapter, show that the field has make a leap forward the past decade toward these goals and that these promises may be fulfilled very soon

Predictive Modeling for Intelligent Maintenance in Complex Semiconductor Manufacturing Processes.

Semiconductor fabrication is one of the most complicated manufacturing processes, in which the current prevailing maintenance practices are preventive maintenance, using either time-based or wafer-based scheduling strategies, which may lead to the tools being either “over-maintained” or “under-maintained”. In literature, there rarely exists condition-based maintenance, which utilizes machine conditions to schedule maintenance, and almost no truly predictive maintenance that assesses remaining useful lives of machines and plans maintenance actions proactively. The research presented in this thesis is aimed at developing predictive modeling methods for intelligent maintenance in semiconductor manufacturing processes, using the in-process tool performance as well as the product quality information. In order to achieve an improved maintenance decision-making, a method for integrating data from different domains to predict process yield is proposed. The self-organizing maps have been utilized to discretize continuous data into discrete values, which will tremendously reduce the computational cost of Bayesian network learning process that can discover the stochastic dependences among process parameters and product quality. This method enables one to make more proactive product quality prediction that is different from traditional methods based on solely inspection results. Furthermore, a method of using observable process information to estimate stratified tool degradation levels has been proposed. Single hidden Markov model (HMM) has been employed to represent the tool degradation process under a single recipe; and the concatenation of multiple HMMs can be used to model the tool degradation under multiple recipes. To validate the proposed method, a simulation study has been conducted, which shows that HMMs are able to model the stratified unobservable degradation process under variable operating conditions. This method enables one to estimate the condition of in-chamber particle contamination so that maintenance actions can be initiated accordingly. With these two novel methods, a methodological framework to perform better maintenance in complex manufacturing processes is established. The simulation study shows that the maintenance cost can be reduced by performing predictive maintenance properly while highest possible yield is retained. This framework provides a possibility of using abundant equipment monitoring data and product quality information to coordinate maintenance actions in a complex manufacturing environment.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58530/1/yangliu_1.pd

Heterogeneous Multi-Robot Cooperation

This report addresses the problem of achieving cooperation within small- to medium- sized teams of heterogeneous mobile robots. I describe a software architecture I have developed, called ALLIANCE, that facilitates robust, fault tolerant, reliable, and adaptive cooperative control. In addition, an extended version of ALLIANCE, called L-ALLIANCE, is described, which incorporates a dynamic parameter update mechanism that allows teams of mobile robots to improve the efficiency of their mission performance through learning. A number of experimental results of implementing these architectures on both physical and simulated mobile robot teams are described. In addition, this report presents the results of studies of a number of issues in mobile robot cooperation, including fault tolerant cooperative control, adaptive action selection, distributed control, robot awareness of team member actions, improving efficiency through learning, inter-robot communication, action recognition, and local versus global control