Car position and orientation based driving skill metric for analytical driving skill index evaluator

This paper proposes an improved driving skill metric incorporating car-track deviation angle and car position off-set correction agility. The main advantage of the method is which it provides more accurate since this research is using more variables in the formulation for the skill metric compared to the old metric by using the same sample group of subjects. This paper reports nearly an average of 95 of improvement for the new skill metric estimation accuracy. The analysis further reveals the metric also has better driving skill index estimation consistency. The proposed method herein offers a means for characterizing driving skill in response to car-track input information and improving the intelligent behaviours of car support system

Human driving skill for human adaptive mechatronics applications by using neural network system

The existence of the new improvement system for Human Machine System (HMS) is called as Human Adaptive Mechatronic (HAM) system. The main difference between these two systems is the relationship between human and machine in the system. HMS is one way relationship between human and machine while HAM is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristics. As a part of mechatronics system, HAM has an ability to adapt with human skill to improve the performance of machine. Driving a car is one of the examples of application where HAM can be applied. One of the important elements in HAM is the quantification of human skill. Therefore, this project proposed a method to quantify the driving skill by using Artificial Neural Network (ANN) system. Feedforward neural network is used to create a multilayer neural network and five models of network were designed and tested using MATLAB Simulink software. Then, the best model from five models is chosen and compared with other method of quantification skill for verification. Based on results, the critical stage in designing the network of the system is to set the number of neurons in the hidden layer that affects an accuracy of the output

Fuzzy Logic Control System Stability Analysis Based on Lyapunov’s Direct Method

A stability analysis method for nonlinear processes controlled by Takagi- Sugeno (T-S) fuzzy logic controllers (FLCs) is proposed. The stability analysis of these fuzzy logic control systems is done in terms of Lyapunov’s direct method. The stability theorem presented here ensures sufficient conditions for the stability of the fuzzy logic control systems. The theorem enables the formulation of a new stability analysis algorithm that offers sufficient stability conditions for nonlinear processes controlled by a class of T-S FLCs. In addition, the paper includes an illustrative example that describes one application of this algorithm in the design of a stable fuzzy logic control system

A Tutorial on Learning Human Welder\u27s Behavior: Sensing, Modeling, and Control

Human welder\u27s experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder\u27s behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder\u27s intelligence are surveyed, including sensing of the weld pool, modeling of the welder\u27s adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder\u27s intelligence into robotic welding system. Finally future research directions in this field are presented

Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)

Çevrimiçi ( XIV, 67 pages

Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics

Nowadays, the Human Machine System (HMS) is considered to be a proven technology, and now plays an important role in various human activities. However, this system requires that only a human has an in-depth understanding of the machine operation, and is thus a one-way relationship. Therefore, researchers have recently developed Human Adaptive Mechatronics (HAM) to overcome this problem and balance the roles of the human and machine in any HMS. HAM is different compared to ordinary HMS in terms of its ability to adapt to changes in its surroundings and the changing skill level of humans. Nonetheless, the main problem with HAM is in quantifying the human skill level in machine manipulation as part of human recognition. Therefore, this thesis deals with a proposed formula to quantify and classify the skill of the human operator in driving a car as an example application between humans and machines. The formula is evaluated using the logical conditions and the definition of skill in HAM in terms of time and error. The skill indices are classified into five levels: Very Highly Skilled, Highly Skilled, Medium Skilled, Low Skilled and Very Low Skilled. Driving was selected because it is considered to be a complex mechanical task that involves skill, a human and a machine. However, as the safety of the human subjects when performing the required tasks in various situations must be considered, a driving simulator was used. The simulator was designed using Microsoft Visual Studio, controlled using a USB steering wheel and pedals, as was able to record the human ii path and include the desired effects on the road. Thus, two experiments involving the driving simulator were performed; 20 human subjects with a varying numbers of years experience in driving and gaming were used in the experiments. In the first experiment, the subjects were asked to drive in Expected and Guided Conditions (EGC). Five guided tracks were used to show the variety of driving skill: straight, circular, elliptical, square and triangular. The results of this experiment indicate that the tracking error is inversely proportional to the elapsed time. In second experiment, the subjects experienced Sudden Transitory Conditions (STC). Two types of unexpected situations in driving were used: tyre puncture and slippery surface. This experiment demonstrated that the tracking error is not directly proportional to the elapsed time. Both experiments also included the correlation between experience and skill. For the first time, a new skill index formula is proposed based on the logical conditions and the definition of skill in HAM

Machine-human Cooperative Control of Welding Process

An innovative auxiliary control system is developed to cooperate with an unskilled welder in a manual GTAW in order to obtain a consistent welding performance. In the proposed system, a novel mobile sensing system is developed to non-intrusively monitor a manual GTAW by measuring three-dimensional (3D) weld pool surface. Specifically, a miniature structured-light laser amounted on torch projects a dot matrix pattern on weld pool surface during the process; Reflected by the weld pool surface, the laser pattern is intercepted by and imaged on the helmet glass, and recorded by a compact camera on it. Deformed reflection pattern contains the geometry information of weld pool, thus is utilized to reconstruct its $3$D surface. An innovative image processing algorithm and a reconstruction scheme have been developed for (3D) reconstruction. The real-time spatial relations of the torch and the helmet is formulated during welding. Two miniature wireless inertial measurement units (WIMU) are mounted on the torch and the helmet, respectively, to detect their rotation rates and accelerations. A quaternion based unscented Kalman filter (UKF) has been designed to estimate the helmet/torch orientations based on the data from the WIMUs. The distance between the torch and the helmet is measured using an extra structure-light low power laser pattern. Furthermore, human welder\u27s behavior in welding performance has been studied, e.g., a welder`s adjustments on welding current were modeled as response to characteristic parameters of the three-dimensional weld pool surface. This response model as a controller is implemented both automatic and manual gas tungsten arc welding process to maintain a consistent full penetration

Virtualized Welding Based Learning of Human Welder Behaviors for Intelligent Robotic Welding

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots

Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models