Manipulator control board

The Mechatronics Research Centre (MRC) owns a small scale robot manipulator called aMini-Mover 5. This robot arm is a microprocessor-controlled, six-jointed mechanical armdesigned to provide an unusual combination of dexterity and low cost.The Mini-Mover-5 is operated by a number of stepper motors and is controlled by a PCparallel port via a discrete logic board. The manipulator also has an impoverished array ofsensors.This project requires that a new control board and suitable software be designed to allow themanipulator to be controlled from a PC. The control board will also provide a mechanism forthe values measured using some sensors to be returned to the PC.On this project I will consider: stepper motor control requirements, sensor technologies,power requirements, USB protocols, USB hardware and software development and controlrequirements (e.g. sample rates).In this report we will have a look at robots history and background, as well as we willconcentrate how stepper motors and parallel port work

Revenue management under general discrete choice model of consumer behavior

Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.