Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

With regard to both human and robot capabilities, human-robot interaction provides several benefits, and this will be significantly developed and implemented. This work focuses on the development of real-time external force/position control used for human-robot interaction. The force-controlled robotic system integrated with proportional integral control was performed and evaluated to ensure its reliably and timely operational characteristics, in which appropriate proportional integral gains were experimentally adopted using a set of virtual crank-turning tests. The designed robotic system is made up of a robot manipulator arm, an ATI Gamma multi-axis force/torque sensor and a real-time external PC based control system. A proportional integral controller has been developed to provide stable and robust force control on unknown environmental stiffness and motion. To quantify its effectiveness, the robotic system has been verified through a comprehensive set of experiments, in which force measurement and ALTER real-time path control systems were evaluated. In summary, the results indicated satisfactorily stable performance of the robot force/position control system. The gain tuning for proportional plus integral control algorithm was successfully implemented. It can be reported that the best performance as specified by the error root mean square method of the radial force is observed with proportional and integral gains of 0.10 and 0.005 respectively.

One of the challenging issues in robotics is to give a mobile robot the ability to recognize its initial pose ( position and orientation) without any human help. In this paper, the components of a mobile robot will be described in addition to the specification of the sensor that will be used. Then, the map of the environment  will be defined since it is pre-defined and stored in the memory of the robot. After that, a localization algorithm has been designed, analysed and implemented to develop the ability of a mobile robot to  recognize its initial pose. Finally, the final results that have been taken practically will discussed. These result will be divided into two main sub-sections; the first section describes the particles distribution over the working environment and their position update over a number of iterations. Second section will shows the update in the importance weight values over a number of iterations and for three different number of particles.

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure and an unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. In this research a fault detection system based on statistical control chart has been designed. An experimental investigation was accomplished using the PUMA 560 robot. Vibration signals are captured from the robot when it executes a repetitive task and then some statistical features are extracted from the signals, by utilising a developed data acquisition system based on National instruments hardware and software. The extracted vibration features, which are related to the robot healthy and faulty states, have subsequently been used for building and testing a statistical control chart. The chart has been validated using part of the measured data set, not used within the design stage, which represents the robot operating conditions. Validation results indicate the successful detection of faults at the early stages using the key extracted parameters.

In this paper, A* path planning algorithm has been represented for a mobile robot to be able to follow a constructed path from its current position to a specified goal within its environment. To ensure that the mobile robot follow the constructed path by path planning algorithm, a motion control algorithm has been built. In the same time, to detect static obstacles and avoid collision with them, an obstacle detection algorithm has been used as a final algorithm that will be used as a part of the whole system to give the robot the ability to move from its initial known position to a specific goal in an optimum way.

This paper deals with on-line (Real-time) multi- resolution signal analysis using wavelet transform by means of graphical programming using LabVIEW. A system for wavelet analysis has been designed based on Arduino-Uno board interfaced to LabVIEW. To achieve the interfacing LIFA has been used. Firstly, the noise from the signal was remove using wavelet transform, and then the de-noised signal analyzed to multi-level frequency bands. A Matlab code to do wavelet analysis was written in a Matlab script node in LabVIEW. To test this system, a vibration signal form robotic arm was captured and analyzed using this system, and the result utilized to establish if there is fault in the robot. The result showed that multi-resolution analysis can be achieved efficiently using this system and can be applied in many applications. 

The condition monitoring of machines has long been accepted as a most effective solution in avoiding sudden shutdown and to detect and prevent failures in complex systems. Signal capture and analysis, and feature extraction and classification represent the main tasks in building any monitoring system. Signal processing plays a significant role in condition monitoring and the fault diagnosis process. Many types of signals can be used in the condition monitoring of machines, such as vibration, electrical and sound signals. Processing these signals in an appropriate way is crucial in extracting the most salient features related to specific types of faults. A variety of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. The main focus of this research is a consideration of signal processing techniques which can be applied in condition monitoring, and to identify their advantages and disadvantages. Then, the wavelet transform is discussed in detail. After that, a monitoring system based on multi-resolution analysis using the wavelet transform is successfully simulated using LabVIEW and Mat lab capabilities. The results show that the differences between healthy and faulty signals can be effectively detected using the wavelet transform.

This paper presents an outline of human-human interaction (HHI) to establish a framework to understand how a behaviour based approach can be developed in the design of a human-robot interaction (HRI) strategy. To approach the conceptual design guidelines for a HRI control strategy, the human dynamic model of human behaviour during performing an object transfer task without any types of communication has been strategically analysed. The extended crossover model proposed by McRuer [1] has been applied to identify the human arm characteristic models under various conditions when executing cooperative tasks. A set of compliant-object-handover tasks have been designed and conveyed (based on Box-Behnken test design[1]), along with the influence variables affecting the human forces, consisting of mass, friction and target displacement. According to the results, the McRuer crossover models were appropriately estimated and reported to be in good matching with the actual experimental data. Additionally, it can be found that a loop gain (KH) is inversely proportional to the object distance moved and is associated with a faster response. The best-fit percentages of human force profiles are almost 100%; therefore, the proposed models can be used to present the human arm characteristics effectively.

Industrial robots are commonly used in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots, and they represent the basic building blocks of the production sector. The ability to continuously monitor the status and condition of robots has become an important research issue in recent years and is now receiving considerable attention. Many types of signals can be used for the detection of faults in industrial robots, such as vibrations and acoustic emissions. However, the most important thing is how these signals are processed in appropriate ways in order to extract the most salient features related to specific robot faults. Thus, signal processing step plays a significant role in the fault detection process for any machine and especially for industrial robots. Therefore, the wavelet transform has been utilized in this research for the detection of faults in an industrial robot. In order to build an accurate fault detection system a number of parameters in the wavelet analysis need to be adjusted carefully. The main focus of this research is to discuss the appropriate selection of these parameters, and then to build a fault detection system for the robot based on LabView programming.

Industrial robots are commonplace in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots and they represent the basic building blocks of the production sector. Recent developments involve using robots cooperatively with production line operatives, and they are now routinely used in healthcare, nuclear plants, and other hazardous environments. Regardless of their application there are significant implications for operator safety in the event of a robot malfunction or failure, and the resulting downtime has a significant impact on productivity in manufacturing. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. However, in this research a fault detection system based on multi-resolution analysis using wavelet transform was successfully designed and applied using LabView programming.

This paper presents an outline of human-human interaction to establish a framework to understand how a behaviour based approach can be developed in the design of a human-robot interactive strategy. To approach the conceptual design guidelines for an interactive human-robot strategy, the mathematical model of human behaviour during transferring the compliant object to a receiver without any types of communication has been strategically analysed. The Auto Regressive Moving Average with Exogenous Input (ARMAX) system identification has been applied to identify the human arm model. A set of experiments have been designed (based on BoxBehnken), along with the influence variables affecting the human forces, which consist of mass, friction and target displacement. The estimated ARMAX models were shown to be good matching with the actual experimental data, where the best-fit percentages of human force profiles are between 88.73%-97.2%; the proposed models can then be used to present the human arm characteristics effectively.

Gears are one of the most important parts of any mechanical transmission system, and in order to achieve reliable operation effective monitoring techniques must be employed. Predictive health monitoring (PHM) systems are currently gaining in popularity due to their effectiveness in providing robust information about the system condition and reducing maintenance costs. However, PHM systems require reliable monitoring techniques, such as vibration, acoustic emission, and oil debris analysis. These techniques have been studied in recent years to discover which can best support the operation of PHM systems in tracing the condition of the operating transmission. These studies have shown the need to apply intelligent algorithms in order to benefit from the advantage of each technique in classifying faults and predicting the onset of failure. This paper presents a new online PHM system for monitoring different gear faults using vibration analysis and autoregressive (AR) algorithms. The intelligent health monitoring system (IHMS) has been implemented on a back-to-back gearbox and can be adapted to monitor the behaviour of transmission systems in automotive, aircraft, wind turbine, and industrial machinery. The study describes the operation of the online IHMS under variable conditions and its capability in detecting transmission gear defects and thus preventing sudden unexpected failure. The results of the experimental test prove the system's capability and support the recent trend of using IHMSs in PHM strategies.

In this paper, subjective measures were considered to evaluate the level of presence of human through different visual/tactual feedback modes in the VisiTact system [1]. An MRT test along with a form of questionnaire were used to examine the performance of human being subjected to a virtual environment with kinaesthetic feedback. A slight indication of a link between the spatial awareness of the participants and their performance with virtual environment in the contact task. No significant differences in terms of how well the participants accomplished the tasks.

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot in a cluttered environment to navigate successfully to a goal while avoiding obstacles is a challenging problem. This paper presents a new behaviour-based architecture design for mapless navigation. The architecture is composed of several modules and each module generates behaviours. A novel method, inspired from a visual homing strategy, is adapted to a monocular vision-based system to overcome goal-based navigation problems. A neural network-based obstacle avoidance strategy is designed using a 2-D scanning laser. To evaluate the performance of the proposed architecture, the system has been tested using Microsoft Robotics Studio (MRS), which is a very powerful 3D simulation environment. In addition, real experiments to guide a Pioneer 3-DX mobile robot, equipped with a pan-tilt-zoom camera in a cluttered environment are presented. The analysis of the results allows us to validate the proposed behaviour-based navigation strategy.

The paper proposes an adaptive neural kinematic controller to guide a National Instrument starter kit robot, which is a nonholonomic differential drive mobile robot, to follow a predefined trajectory. The structure of the controller is based on a neural network topology. The Multi-Layer Perceptron MLP neural network was used to design and implement the controller. The error back propagation algorithm was used to train the neural network to learn the behavior of the mobile robot kinematic model. The neural kinematic controller is trained offline and then the weights of the neural network are adjusted online in order to find the required linear and angular velocities to guide the robot throughout the reference trajectory. The controller is implemented in LabVIEW2011 software and then deployed to the mobile robot platform to allow for autonomous navigation. The simulation results and experimental results show that the proposed controller can successfully navigate the robot along the required path.