To Örebro University

Inspiration from biological systems suggests that robots should demonstrate same level of capabilities that are embedded in biological systems in performing safe and successful interaction with the humans. The major challenge in physical human robot interaction tasks in anthropic environment is the safe sharing of robot work space such that robot will not cause harm or injury to the human under any operating condition.

Embedding human like adaptable compliance characteristics into robot manipulators can provide safe physical human robot interaction in constrained motion tasks. In robotics, this property can be achieved by using active, passive and semi active compliant actuation devices. Traditional methods of active and passive compliance lead to complex control systems and complex mechanical design.

In this thesis we present compliant robot manipulator system with semi active compliant device having magneto rheological fluid based actuation mechanism. Human like adaptable compliance is achieved by controlling the properties of the magneto rheological fluid inside joint actuator. This method offers high operational accuracy, intrinsic safety and high absorption to impacts. Safety is assured by mechanism design rather than by conventional approach based on advance control. Control schemes for implementing adaptable compliance are implemented in parallel with the robot motion control that brings much simple interaction control strategy compared to other methods.

Here we address two main issues: human robot collision safety and robot motion performance.We present existing human robot collision safety standards and evaluate the proposed actuation mechanism on the basis of static and dynamic collision tests. Static collision safety analysis is based on Yamada’s safety criterion and the adaptable compliance control scheme keeps the robot in the safe region of operation. For the dynamic collision safety analysis, Yamada’s impact force criterion and head injury criterion are employed. Experimental results validate the effectiveness of our solution. In addition, the results with head injury criterion showed the need to investigate human bio-mechanics in more details in order to acquire adequate knowledge for estimating the injury severity index for robots interacting with humans.

We analyzed the robot motion performance in several physical human robot interaction tasks. Three interaction scenarios are studied to simulate human robot physical contact in direct and inadvertent contact situations. Respective control disciplines for the joint actuators are designed and implemented with much simplified adaptable compliance control scheme.

The series of experimental tests in direct and inadvertent contact situations validate our solution of implementing human like adaptable compliance during robot motion and prove the safe interaction with humans in anthropic domains.

Human robot interaction (HRI) tasks requires robots to have safe sharing of work space and to demonstrate adaptable compliant behavior enabling eminent collision safety as well as maintaining high position accuracy. Robot compliance control normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We proposed a unique method using semi-active compliant actuation mechanism having magneto-rheological (MR) fluid based actuator that introduces reconfigurable compliance characteristics into the robot joints. This enables high intrinsic safety coming from fluid mechanics as well as, it offers simpler interaction control strategy compared to other concurrent approaches. In this studies, we have described three essential modes of motions required for physical human system interaction. Furthermore, we have demonstrated robot collision safety in terms of static collision and experimentally validates the performance of robot manipulator enabling safe human robot interaction.

The success of human robot interaction (HRI) tasks is characterized by evaluating robot performance in terms of collision safety and position accuracy. Hence, both position accuracy and collision safety are equally indispensable. HRI refers to cognitive as well as physical interaction. Cognitive human robot interaction based on perception and awareness where as physical human robot interaction demands direct contact with the humans exhibiting adaptable compliant behavior. Therefore, development of ideal safe robot manipulator having adaptable compliant actuation is inevitable. Adaptable compliance can be achieved by using active compliant actuation requiring various sensor data or by using passive compliant devices with high mechanical complexity. We present magneto rheological fluid based compliant actuation mechanism introducing adaptable compliance directly into robotic joint with much simpler interaction control and higher intrinsic safety originating from fluid mechanics. In this study, we have discussed adaptable compliance in terms of essential modes of motion for safe physical HRI and evaluated the safety performance of our robot for static collision testing and dynamic collision testing based on impact force and head injury criterion. Finally, the experimental results validate the significance of our proposed method for both human robot collision safety and high position accuracy.

Robots have recently been foreseen to work side by side and share workspace with humans in assisting them in tasks that include physical human-robot (HR) interaction. The physical contact with human tasks under uncertainty has to be performed in a stable and safe manner [6]. However, current industrial robot manipulators are still very far from HR coexisting environments, because of their unreliable safety, rigidity and heavy structure. Besides this, the industrial norms separate the two spaces occupied by a human and a robot by means of physical fence or wall [9]. Therefore, the success of such physical HR interaction is possible if the robot is enabled to handle this interaction in a smart way to prevent injuries and damages.

Human robot interaction (HRI) in constrained motion tasks requires robots to have safe sharing of work space and to demonstrate adaptable compliant behavior Compliance control of industrial robots, normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We present a unique method using a novel actuation mechanism based on magneto-rheological fluid (MRF) that incorporates reconfigurable compliance directly into the robot joints. This brings much simple interaction control strategy compared to other antagonistic methods. In this studies, we have described three essential modes of motions required for physical human system interaction. Then we have discussed their respective control disciplines. Finally, we have presented functional performance of reconfigurable MRF actuation mechanism in constrained motion tasks by simulating various HRI scenarios.

Impedance control and compliant behavior for safe human-robot physical interaction of industrial robots normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We present another approach using a novel actuation mechanism based on magneto-rheological fluid (MRF) that incorporates variable stiffness directly into the joints. In this paper, we have investigated and analyzed principle characteristics of MRF actuation mechanism and presented the analytical-model. Then we have developed the static and dynamic model based on experimental test results and have discussed three essential modes of motion needed for human-robot manipulation interactive tasks.

Biofouling build-up on submerged structures such as ships, petroleum and gas storage tanks, electric power plants, bridges, oil rigs etc. is a major problem that affects the surface material of the structure, the eventual hydrodynamic quality of the surface and in some cases the efficacy of cooling systems. Underwater cleaning is a solution to maintain submerged structures in order to assure proper functioning for as long as possible. Consequently, there has been an increased interest in the development of new technologies for robotised underwater cleaning systems.

This thesis presents a new concept of a flexible crawling mechanism for an industrial underwater cleaning robot, which is evaluated from the viewpoint of its capability to work underwater, scanning the desired surface, and perform a cleaning task. The main research questions investigated in this thesis are: (1.) how to select the most important features in choosing the platform mechanism to fulfil the surface scanning operation, (2.) how to design the platform in order to bear the forces related to the cleaning task, (3.) how to maintain surface contact throughout cleaning, (4.) determine the significant parameters to be monitored in order to ensure stable positioning on the surface during the cleaning process and (5.) how to develop the control of actuators to realise the locomotion and to follow the desired trajectory.

This thesis begins with a classification and discussion of the available solutions for underwater operation, taking into consideration the benefits and drawbacks, overall efficiency and environmental and human safety issues. From this survey, an underwater mobile robotic platform is designed to address the main requirements and industrial needs. Further, a study and simulation of its mobility and stability on the surface is performed and a complete scenario of the entire cleaning operation is presented. In addition, an overview of the required sensors and the control system is given. Finally, a new robotised system was developed to clean underwater surfaces with minimum active degrees of freedom. A successful simulation and real experimental results were obtained with a simplified lab-scale prototype. The thesis concludes with a summary of future works and outlook for the growing field of underwater cleaning robots.

This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.

Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: (i) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and (ii) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: (i) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and (ii) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non controlled but structured environments where there is a high chance for the sensor to be detecting objects with flat surfaces (e.g., walls). These newly proposed algorithms are thus specially designed and optimized for inferring precisely the angular orientation of the Lidar sensor with respect to the detected object, a feature that is beneficial especially for indoor navigation purposes.

This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

We consider the analysis and design of three different types of nonlinear observers for dynamic Takagi-Sugeno fuzzy systems. Our approach is based on extending existing nonlinear observer schemes, namely Thau-Luenberger and sliding mode observers, to the case of interpolated multiple local affine linear models. Then linear matrix inequality based techniques are used for observer analysis and design.

This article addresses the fast solution of a Quadratic Program underlying a Linear Model Predictive Control scheme that generates walking motions. We introduce an algorithm which is tailored to the particular requirements of this problem, and therefore able to solve it efficiently. Different aspects of the algorithm are examined, its computational complexity is presented, and a numerical comparison with an existing state of the art solver is made. The approach presented here, extends to other general problems in a straightforward way. © 2009 IEEE.

In the present paper we describe the design of a fuzzy gain scheduler for tracking a reference trajectory of a nonlinear autonomous system. The proposed fuzzy gain scheduling method has two major advantages over the existing crisp gain scheduling methods. First, it provides a general and formally motivated method for the interpolation of available local control laws into a global gain scheduling control law. Second, the method for determining the weights of the local control laws in the global gain scheduling control law is general and computationally efficient. It is shown that a fuzzy gain scheduler can be designed such that robust asymptotic stability is met. Finally, an LQR control design based method is presented

This paper presents an approach to complex system recovery based on a fuzzy specification method. The method can be applied when controllers of different types and makes are connected in a common control architecture. The method allows a controller B to trigger a recovery operation on a controller A when B has the recovery specification of A. The approach has important potential applications in industry, e.g. as a possible complement to PLC standards such as IEC1131, and to the design of hybrid and complex control systems

This paper introduces a hybrid fuzzy-Boolean finite state machine (HFB FSM) model for ontological control. Ontological control is a novel type supervisory control that deals with the problems of error detection and recovery in complex control systems. The HFB FSM is used as a specification method for the problem of recovery when an autonomous control system encounters unexpected changes in its environment. The method allows a controller B (the ontological controller) to trigger a recovery operation on controller A when B has the recovery specification of A. The approach has important potential applications in industry

A current-independent torque equation for the permanent-magnet synchronous motor (PMSM) aiming at direct-drive servo applications is derived from a first principles model. Instead of measuring currents, all required control parameters are derived from optical incremental encoder measurements. The results are verified on a real system in test series showing the effect of static friction and proving the obtained torque model.

Robot manipulators have a complex and highly nonlinear dynamics, accompanied with a high degree of uncertainty. These properties make them difficult for time-optimal control. The theory of sliding mode control can provide methods, able to cope with the uncertainty and nonlinearity in the system. However, besides the chattering problem it does not provide

time-optimal behavior. The optimal control theory provides the appropriate design methodology for minimum-time control, but the designed system lacks robustness. In this thesis we combine these two approaches to obtain new control techniques, which have the robust properties of the sliding mode control and a performance, close to the time-optimal control. Two methods for minimum-time sliding mode control based on the concept of maximum slope sliding line are developed with a theoretical proof of their properties.

In the time-optimal sliding mode control we prove that the time-optimal switching line of a simple linear system (double integrator) can be used as a sliding surface for a complex second order nonlinear system (robot manipulator) if the control gain is sufficiently high. Optimal

performance is achieved by scaling the surface in such way that the maximum control action is efficiently used.

The fuzzy minimum-time sliding mode control is developed employing a Takagi-Sugeno

fuzzy model for the sliding surface. We demonstrate that designs, based on a single sliding line tend to be conservative, due to the nonlinearities in the robot's dynamics. The Takagi-Sugeno model represents the maximum slope sliding lines for different values of the joint angles taking into account the variation in the gravity and inertia terms. This gives a convenient way to provide adaptation and incorporate additional knowledge in the controller design.

Design procedures for all the methods are developed and evaluated in simulation and in experiments with real robot manipulators.

Our approach for near time-optimal control is based on Takagi-Sugeno fuzzy model of the maximum slope SMC sliding surface as an adaptive technique for tuning the current slope of the sliding surface to the maximum feasible slope depending on the current state of system. The stability conditions of this method are proved and respective measures about the feasible maximum slope are presented. Experimental results demonstrate the system behaviour.

The problem of food- and water quality assessment is important for many practical applications, such as food industry and environmental monitoring. In this article we present a method for fast online quality assessment based on electronic tongue measurements. The idea is implemented in two steps. First we apply a fuzzy clustering technique to obtain prototypes corresponding to good and bad quality from a set of training data. During the second, online step we evaluate the membership of the current measurement to each cluster and make a decision about its quality. The result is presented to the user in a simple and understandable way, similar to the concept of traffic light signals. Namely, good quality is indicated with by a green light, bad quality with a red one, and a yellow light is a warning signal. The approach is demonstrated in two case studies: quality assessment of drinking water and baby food.

In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations.

Taking into consideration the modeling case at par with uncertain nonlinear systems, the implementation of neural network technique is contributed in the complex way of dealing the appropriate coefficients of the fuzzy equations. We use the neural network method to approximate Z-number coefficients of the fuzzy equations.

A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.

This work presents a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. We use a novel approach to the design consisting of two steps: 1) Mamdani-type of fuzzy rules to compute each of the desired horizontal velocity corresponding to the desired values for the attitude angles and the main rotor collective pitch; and 2) a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. The performance of the combined linguistic/model-based controller is evaluated in simulation and shows that the proposed design method achieves its intended purpose

This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a robust controller design method is proposed. The sufficient conditions and the design procedure are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated of a visual-servoing control problem.

The main contribution of this paper is a comparison of different machine learning algorithms for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard using measurements of road surface vibrations and magnetic field disturbances caused by vehicles. The algorithms considered are logistic regression, neural networks, and support vector machines. They are evaluated on a large dataset, consisting of 3074 samples and hence, a good estimate of the actual classification rate is obtained. The results show that for the considered classification problem logistic regression is the best choice with an overall classification rate of 93.4%.

This paper presents a control structure selection guideline based on Type-1 and Type-2 T-S fuzzy models for multi-input-multi-output (MIMO) processes. By virtue of relative normalized gain array (RNGA), the interactions between process inputs and outputs are analyzed to determine a decentralized control configuration as well as a richer control structure for an MIMO process controller design. Simple calculating procedures based on both Type-1 and Type-2 T-S fuzzy models are provided. An MIMO process is utilized as an example to demonstrate effectiveness and robustness of the proposed method.

In order to facilitate decentralized fuzzy controller designs for multi-input-multi-output (MIMO) processes, this paper presents a novel manner, called effective Takagi-Sugeno (T-S) fuzzy model (ETSM), to describe the interactions among the loops. For a certain control-loop of an MIMO process, in terms of relative normalized gain array (RNGA) based loop pairing criterion, simple calculating procedure is given to obtain an ETSM based on its individual open-loop T-S fuzzy model. With the ETSMs of control-loops, an MIMO process can be approximately regarded as multiple non-interacting single loops such that each local controller of a decentralized control system can be independently designed using linear single-input-single-output (SISO) control algorithms. Compared with the existing decentralized fuzzy control methods adding extra terms to individual open-loop models to characterize interactions, ETSM is a practical and low-cost way. While compared with the existing effective transfer function (ETF) methods, ETSM is an extension that can proceed without requiring exact process mathematical functions, and lays a basis to develop robust controller since fuzzy system is strong in handling uncertainties. In case study, a nonlinear MIMO process is used as an example to demonstrate the effectiveness of the proposed ETSM method.

In terms of relative normalized gain array (RNGA) criterion, this paper presents an interaction analysis and loop pairing method for multi-input-multi-output (MIMO) processes described by Type-2 Takagi-Sugeno (T-S) fuzzy models. Type2 fuzzy system offers a significant improvement on traditional (Type-1) fuzzy systems to handle the uncertainties. For each individual loop in the MIMO process, the steady-state gain and normalized integrated error can be derived from its Type-2 T-S fuzzy model through simple formulae, and the pairing results can then be obtained according to the rules of RNGA criterion. This method can be applied to measure the interactions and determine the control structure for multivariable control system design when the mathematical functions are unavailable and a large number of uncertainties present. The simulation proves that the proposed method can provide more accurate results than the existing RNGA paring method based on Type-1 fuzzy model under the influence of uncertainties.

In order to better handle the coupling effects when controlling multiple-input multiple-output (MIMO) systems, taking the decentralized control structure as the basis, this paper proposes a sparse control strategy and a decoupling control strategy. Type-1 and type-2 Takagi-Sugeno (T-S) fuzzy models are used to describe the MIMO system, and the relative normalized gain array (RNGA) based criterion is employed to measure the coupling effects. The main contributions include: i). compared to the previous studies, a manner with less computational cost to build fuzzy models for the MIMO systems is provided, and a more accurate method to construct the so-called effective T-S fuzzy model (ETSM) to express the coupling effects is developed; ii). for the sparse control strategy, four indexes are defined in order to extend a decentralized control structure to a sparse one. Afterwards, an ETSM-based method is presented that a sparse control system can be realized by designing multiple independent single-input single-output (SISO) control-loops; iii). for the decoupling control strategy, a novel and simple ETSM-based decoupling compensator is developed that can effectively compensate for both steady and dynamic coupling effects. As a result, the MIMO controller design can be transformed to multiple non-interacting SISO controller designs. Both of the sparse and decoupling strategies allow to use linear SISO control algorithms to regulate a closely coupled nonlinear MIMO system without knowing its exact mathematical functions. Two examples are used to show the effectiveness of the proposed strategies.

tEffective model is a novel tool for decentralized controller design to handle the interconnected inter-actions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effectiveTakagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple cal-culations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interactingequivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently thedecentralized controller design can be converted to multiple independent single-loop controller designs,and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i)Compared to the existing T-S fuzzy model based decentralized control methods using extra terms tocharacterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effectivemodel methods using linear transfer functions, ETSM can be carried out without requiring exact mathe-matical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful tohandle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provideobjective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs’ superiority overthe effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs interms of robustness. A multi-evaporator refrigeration system is employed to validate the practicabilityof the proposed methods.

We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. This chapter describes three different methods for grasp recognition for a human hand. A human operator wearing a data glove instructs the robot to perform different grasps. For a number of human grasps the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using time as input parameter and joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are compared with each other. In the first method, the test grasp is compared with model grasps using the difference between the model outputs. The second method deals with qualitative fuzzy models which used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning.

In this paper we address the problem of recognition of human grasps for five-fingeredrobotic hands and industrial robots in the context of programming-by-demonstration. The robot isinstructed by a human operator wearing a data glove capturing the hand poses. For a number ofhuman grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzyclustering and Takagi-Sugeno (TS) modeling. This so-called time-clustering leads to grasp modelsusing time as input parameter and fingertip positions as outputs. For a sequence of grasps thecontrol system of the robot hand identifies the grasp segments, classifies the grasps andgenerates the sequence of grasps shown before. For this purpose, each grasp is correlated with atraining sequence. By means of a hybrid fuzzy model the demonstrated grasp sequence can bereconstructed.

Robot skills are low-level motion and/or grasping capabilities that constitute the basic building blocks from which tasks are built. Teaching and recognition of such skills can be done by Programming-by-Demonstration approach. A human operator demonstrates certain skills while his motions are recorded by a data-capturing device and modeled in our case via fuzzy clustering and Takagi-Sugeno modeling technique. The resulting skill models use the time as input and the operator's actions and reactions as outputs. Given a test skill by the human operator the robot control system recognizes the individual phases of skills and generates the type of skill shown by the operator.

Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.

In order to automate the operations involved in the industries, the implementation of sensors, actuators and logic controllers has become an utter necessity. In this paper, the stress has been laid on the safety of the working personnel by implementing the technique of automation using automated devices and Programmable Logic Controller (PLC). In Oil refineries, we have analyzed the specific zone of an oil collection station and found the necessity of implementing a safer working environment for the working personnel. An oil collecting station is an area in an oil refinery where oil in various forms after refining is stored in large oil tanks. It is from here that the stocks of oil are dispatched to various locations. The oil collecting stations contains large cylindrical tanks which are used to store oil. Oil in various form are sent from the digging zone after refining to the oil collecting station through long pipelines. Taking into account these aspects, safety system have been designed and developed in order to secure that area. In any industry the safety of the working personnel is of first priority. The productivity and efficiency of any organization depends on the efficiency of the working personnel. Hence it is very important to provide a better working environment for the working personnel. So, this project is a trend towards the better productivity and work output of any organization which has been made possible with the implementation of automation.

Image-based phenotyping systems have evolved over the years, and become an integral part of crop and plant science research. Phenotyping systems provide great potential to deliver critical insights, than the conventional destructive field methods. Stable image acquisition and processing is very important to accurately determine the characteristics in general, which further becomes very challenging and non-trivial when mounted over an motor mechanised arm. To address the near associated problems, we investigate on the possibility of applying the Proportional–Integral–Derivative (PID) control algorithm to the present manual setup with an aim to reduce vibration. This study focused towards investigating the active control and stabilization of the external camera shake, that may be induced by the driving motor. Nonetheless, very few researchers have focused on application of control algorithms for agriculture related practices. We validate the active control, and justify the need for the same.Type-2 fuzzy logic is combined with the PID control for better effectiveness. The non-linearity associated with the system is compensated by the type-2 fuzzy logic. The results shows that the active control has been achieved, and the vibration is minimized.

Involvement of automation in manufacturing technology plays an important role in enhancing the quality of process and products. An automated device which replaces manual involvement is an extraordinary contribution to the mankind. In this paper, stress is laid on the designing of an automated drilling system so to perform drilling operation automatically in efficient way. So for this purpose, initially the design of the drilling system setup is crafted using PRO-E software based on the design considerations. Then the actuation and control part is taken care with the help of actuating elements like DC motor, mechanical wheels and by programming it effectively using PLC. Finally the prototype model is developed in order to facilitate drilling operation with ease and accuracy. The drilling operation is performed by the combination of the movements of the drilling system and the base on which workpiece is kept for drill operations.In this paper innovative design and efficient programming have been merge to generate a device which will significantly contribute to the field of production.

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.