In order to successfully integrate circular economy principles into current value chains, it is crucial to ensure the economic sustainability of disassembly processes. Recent scientific and technological innovations, including Large Language Models (LLMs) in artificial intelligence (AI), are greatly accelerating progress in enhancing robotic capabilities. Dismantling is the first step in the re-manufacturing, repair, and recycling processes of end-of-life (EoL) products. Traditionally, this operation is performed manually by operators or by expensive dedicated robotic cells. Although manual disassembly offers flexibility in handling complex situations, it is a time-consuming and labour-intensive process that can negatively affect the health of operators and the cost-effectiveness of the disassembly process. However, robot disassembly presents difficulties in handling complex parts in a flexible manner. Robot application emerges as an automated versatile solution, capable of handling uncertainties in the frequency, quantity, and quality of end-of-life products. A fully automated approach for the removal of clothing zips from garments is proposed. The approach detects the pixels corresponding to the zip using LLM-Based AI techniques to plan the path of the robotic arm. The solution reduces the effort of AI training in industrial applications. Simulations validate the approach and its performance.

The co-transport (collaborative-transport) of deformable materials such as fabrics, composite materials, cables or wires and so on, is a challenging task for robotic applications in industry. The main difficulty lies in the deformability of the material, which can slide, stretch and deform during handling and transport. Common approaches in the literature either take advantage of force sensors to act on the fabric and restore its ideal state, or estimate the deformation state of the fabric with depth images and neural networks (NN). In both cases, issues may arise regarding the effects of force control on the material and the industrial reliability of the NNs, respectively. This paper proposes a method based on the estimation of the deformability constraints of the flexible material to obtain geometric parameters that allow planning the trajectory of a collaborative manipulator for co-transport that guarantees the deformation constraints during transport. By identifying a representative point on the side of the fabric held by the man, the action necessary to restore the desired deformation of the fabric that is initially estimated is planned. Once the material assumes the desired state, the robot's movements are generated to track the human's movement and act in safety conditions. A near-time-optimal control strategy is proposed. Finally, the system is tested in a simulation scenario.

This paper presents a preliminary study considering the design of an adaptive torque allocation policy for electric vehicles combining optimal control with data-driven techniques. The vehicle is equipped with four independent actuated wheels driven by electric motors. The policy aims to control the vehicle powertrain by allocating available power among motors to satisfy the driver control torque request and adjust torque allocated to different motors according to estimated wheels slip ratio change due to terrain varying conditions. A constrained optimal torque allocation algorithm is designed to distribute available power among wheels. In order to adjust the power allocation result, a data-driven adaptive policy is designed to adjust the control allocation parameters and the torque distribution reflecting wheel's operating conditions. The combination of torque allocation and data-driven adaptation policies permits the adjustment of the allocated power according to the wheel/road contact conditions. The algorithm has been tested and validated in simulation, showing the improvement given by the proposed approach compared with respect to the control system neglecting the data-driven adaptation of the torque allocation policy.

The advent of increasingly flexible and adaptive production processes will require equally flexible robotic collaboration. The forthcoming robotic applications must be able to self-adapt to different applications and scenarios and to handle frequent interaction with humans and dynamic environments. Environment perception, object recognition, and trajectory re-planning in a dynamic and changing environment, even with possible human interaction, are features that are not yet all simultaneously available in collaborative robots, and certainly not in industrial robots. This paper proposes some initial results of the authors’ ongoing research on the development of a ROS2-based framework for industrial applications due to which a robotic manipulator equipped with a depth camera is able to have simultaneously perception, recognition and re-planning capabilities in a dynamic and changing environment. This framework is potentially applicable to various industrial robots and depth cameras; here, the first results of the experimental implementation on an Omron TM5-900 collaborative robot equipped with a fixed depth camera will be shown.

The desired path following for an autonomous vehicle can be achieved by ensuring the desired yaw rate response. In conventional methods the control of lateral vehicle dynamics by means of yaw rate-based PID controller has sometimes been investigated but rarely implemented. An autonomous driving system can therefore be achieved by designing a reliable yaw rate controller for the vehicle. In this paper, an autonomous vehicle control architecture and a steering angle PID controller based on the vehicle’s yaw rate measured by a gyroscope is proposed and designed to handle most of the manoeuvres of an autonomous vehicle. It does not require measurements of lateral acceleration and lateral speed. The yaw rate reference is provided as an output by a higher-level system and used in the lower-level control loop. The validity of the proposed approach was tested experimentally on a 1:10 scale autonomous vehicle.

This paper focuses on the study and development of learning algorithms oriented to wear classification and predictive maintenance (PdM) of the cutting tool (CT) of a clamping machine for producing structural steel bars. While several works dedicated to CTs for turning and milling operations, or in general for metal removal operations, also known as subtractive manufacturing processes, can be found in the literature, the phenomena related to cutting with a cutting knife have not been widely treated in the literature. This article intends to focus on the analysis of the latter problem. The objective is to estimate the wear of the CT, a critical component of the steel bar cutting machine. The SVM classifiers were therefore used to classify the wear. For the predictive maintenance purpose, two algorithms were implemented for the prediction of the remaining service life, based on the Degradation Model and the Similarity Model respectively; in the first method, a prediction and state update function were used, while in the second method, a Long Short-Term Memory (LSTM) Neural Network (NN) was used.

Innovative technologies and comfort-energy efficiency measures are nowadays well known and widely spread, and the main issue is to identify those that will be proven to be the more effective and reliable in the long term. With such a variety of proposed measures, the decision-maker has to compensate environmental, energy, financial and social factors to reach the best possible solution that will ensure the maximization of the comfort and energy efficiency of smart buildings satisfying at the same time the building's final user/occupant/owner needs. A work-in-progress methodological position investigates the feasibility of applying new techniques to improve the comfort and energy efficiency in smart buildings so that the maximum possible number of alternative solutions and energy efficiency measures may be considered. A simple example is used to identify the potential strengths and weaknesses of the proposed approach and highlight potential problems that may arise.

The field of Permanent Magnet Synchronous Motors (PMSMs) diagnosis is of research interest because widely used both in the Industrial environment and in electric vehicles. Amongst various Fault Detection (FD) techniques, the Motor Current Signature Analysis (MCSA) received lots of attention because some defecting frequencies may be monitored through the motor currents in case of steady-state functioning. This latter assumption is not always fulfilled, such e.g. in robotic systems driven by PMSMs, where constant speed assumption is unrealistic in most of the cases. Furthermore, MCSA in not suitable for systems working under non-stationary conditions without using advanced processing techniques. This work investigates the use of load torque information for motor diagnostic purposes under not constant speed assumption. Simulations and experimental results are presented regarding the use of the proposed Motor Torque Analysis (MTA) to overcome these limits.

This paper presents current research on automated synthesis in the context of the Holonic Management Tree (HMT) technique. HMT has been currently challenged in robotics, manufacturing, construction, but in general this technique concerns the management of complexity that emerges in cyber-physical systems context. Although effective, the technique lacks a systematic and possibly automatic process of construction of the HMT, which impinges with artificial and extended intelligence concerns. A methodological work-in-progress position for the automation of HMT synthesis is proposed for the first time, and as a first step for an enduring but disruptive research effort.

This paper confronts with a reflection about a deep problem in computational models for cyber-physical systems (CPS). The problem arises in the contact between digital computing and the physical realm, and affects heavily the design, modeling, and implementation of CPS. Problems are exacerbated by the introduction of artificial intelligence and autonomy in industrial applications that have to meet sustainability of solutions, both in technical and societal sense. After a brief review, a new perspective and position on the future of sustainable CPS is addressed, and a pragmatic research path is presented. The RMAS (Relational-model Multi-Agent System) architecture is proposed as a test framework for the deep integration of real-world semantics into the advancements brought about by the digital transformation wave.

PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions.

Infrastructure systems strongly influence contemporary society and increasingly our quality of life depends on it, but unfortunately these infrastructures are ageing and failures are becoming common. Automate maintenance procedures can be a real solution to cope with these problems but several limits have to be overcome yet. In this work, these limits are faced by proposing an alternative UAV that would bring advantages in safe inspection tasks and human-interaction, named Double Propeller Ducted-Fan. An alternative UAV's architecture jointly with a dynamic model and a simplified linear control scheme is here proposed, by taking into account the requirements demand to further commercial development. The validity of the model and the proposed control scheme are evaluated in simulations, also taking into account real disturbances and noise from sensors. The results are encouraging in term of performances, compared with tests carried out with linear controllers in similar UAVs.

Predictive Maintenance (PdM) is one of the key enabling technologies in Industry 4.0. The Factories of the Future will adopt highly automated and interconnected environment where predictive fault detection will have an essential role to ensure efficient and reliable industrial operations. Due to their high efficiency and their low cost Cartesian Robots (CRs) represent one of the widely used automation systems in industry. Their movements and efficiency depends on transmission system and its degradation. However not much has been done in terms of PdM for these robots and very few works tries to deal with this problems. Different failures for those kind of robots are attributable to the transmission system. This work details the effect of the transmission system on the robot electrical actuation according to Motor Current Signal Analysis (MCSA) theory. This analysis propose different tools, used in others disciplines for different purposes, to infer features of the faulty condition. By monitoring the motor current of the CR, after a signal preprocessing, a proper fault index have been investigated in order to detect the functionality state of the transmission system. The preliminary results obtained are encouraging compared to classic spectral analysis. The monitoring and analysis have also been extended to the transient state. All the fault detection tests have been carried out directly on the electric drive mounted on a real industrial CR.

Autonomic computing initiative aimed to develop computer systems capable of self-management in order to overcome the rapidly growing complexity of computing systems management. Similar complexity affects the management and control of the systems in the industry of the future that currently relies on the advancements in cyber-physical systems frameworks. With this position paper, the RMAS architecture is checked against the major properties of autonomic systems. RMAS is proposed as a methodological and technological platform to reduce the barriers that complexity poses to further growth in intelligent automation and control.

Recurrence quantification analysis (RQA) allows to quantify the periodic behavior using recurrence plots instead of deriving information purely from visual analysis. The current study presents a preliminary analysis of stator-current measurements for electric motor fault detection and classification by means of the recurrence quantification theory. Firstly, a preliminary visual inspection of the recurrence plots of stator-current measurements for healthy and faulty electric motors is presented. Thereafter, the following RQ metrics are analyzed: the recurrence rate, the determinism, the divergence, the Shannon entropy, the laminarity and the trapping time. Then, the RQ metrics are used as predictors for fault detection and classification. The classification results (100% fault classification accuracy), which are presented using the linear support vector machine classifier, show that the RQA can be considered as a tool for motor current signature analysis.

This work extends a performance metrics method for the treatability of some classes of problems in manufacturing automation that can be represented as a system-of-systems controlled by a cyber-physical infrastructure. With the use of proper distributed and recursive computing approaches, the complexity of the control of cyber-physical systems can be attacked through a unified and human-centered simple framework that complies with the forthcoming pervasive computing challenges posed by the smart manufacturing scenarios. The aim of this work is to provide the proof of concept of an effective methodology that relies on the decomposition of a production goal into a hierarchical self-similar structure of subgoals for the steering of the system toward improved effectiveness. An implementation of the technique is proposed by means of multiactor and multidatabase paradigms. The simulation of implementation and experimental deployment on low cost embedded device is provided.

This paper gives a novel perspective about the role of the self-similarities in the modelling of system of systems with cyber-physical representation. With the adoption of virtual overlays of dynamically self-adapting holarchies, the emergent characteristics and behaviors of the information and automation systems are mapped into a recursive tree of autonomic components that can be typically realized through holonic multiagent systems. The nested computing structures that are used to achieve a productivity goal are kept simple by means of self-similarities. This simplification provides a viable methodology for the pervasive management and the automated programming of the holonic components. The technique described is suitable for a prompt adoption on a vast class of manufacturing, robotics, mechatronics, and facility management processes, whilst enabling a continuum between humans and machines. An advantage of the proposed method is its agnosticism with respect to most of the specific but heterogeneous technological means adopted in the already existent automation realizations.

This work reviews and recaps some lessons learnt in the industrial cyber-physical systems playground to indicate a viable path towards an effective use of artificial intelligence in distributed automation. The holon concept is used as a pivotal tool towards a simplification of the design and implementation of architectures that exploit recursive patterns and self-similarities to keep the industrial systems-of-systems problem under control, whilst addressing the consistent coupling between the physical environmental and the reasoning processes. The Beer’s viable system model is used as an inspiration towards new viable holistic visions of the production system-of-systems.

The present paper proposes a conceptual framework along with a practical solution for the problem of integrating the performance indicators of production processes with the capabilities of robotic systems and machinery. This will enable technology transfer and development of automated production processes within the new vision of industry 4.0. The proposed methodology is a solution for improving the performance of a manufacturing system that integrates robotics, mechatronics and automation systems at different levels. Performance measurements and bottlenecks detection on critical production paths are performed in order to achieve a dynamic and on-line system improvement. This consists of a pervasive control of the effectiveness of the system-of-systems, by means of a well-defined sequence of specific and minimal corrective actions. The proposed technique is advisable in the complex scenario of decentralized manufacturing. The nature of the solution is context-dependent and scalable.

The complexity of cyber-physical systems (CPSs) poses new challenges in their design, model checking and maintenance. The hardware and software designers are in search, more than ever, for simple and interoperable approaches that render the complexity of CPSs a treatable matter. In this work, database language is suggested as an enabling technology and a lean technique to the purpose. An example with best available embedded database technology is conducted by means of a deployment test on tiny embedded electronics.

In this paper we present a swarm intelligence approach to tackle the optimal sizing problem of all the microgrid (MG) components. A model has been built for a grid-connected MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of energy savings benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using artificial bee colonies (ABC) algorithm. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.

In recent years several statistical methods have been applied to condition monitoring of various processes under linearity and stationarity assumptions. However most of the actual industrial processes, e.g. in the chemical sector, are strongly nonlinear. Furthermore the hypothesis of data Gaussian distribution does not often hold, thus causing a decrease of the fault detection accuracy. In this paper a Switching Linear Regression (SLR) approach is firstly proposed in a fault detection scenario. The basic idea is to estimate different Linear Regression models through an arbitrary clustering algorithm and then switching among these models. The developed algorithm allows to deal with nonlinear processes. The proposed fault detection approach is applied to two simulated test bench and on the Tennessee Eastman process benchmark. Furthermore, compared with the Linear Regression algorithm, SLR shows better performance in terms of fault detection accuracy.

The present paper proposes an effective metrics for production efficiency and a bottleneck detection algorithm of recursive nature for its application on lightweight embedded systems on board of the robotics and automation components of the factory of the future. The proposed methodology is particularly suited if the fractal paradigm is applied to the factory seen as a complex system of systems but with relevant self-similarities across the several layers of components and structures from the shop-floor up to the enterprise level. A performance test has been conducted to demonstrate the viability of the technology for tiny embedded devices with the use of declarative embedded database language. Due to the high scalability of the algorithm and its simplicity, it seems suitable also for the robotic cloud paradigm, where constituent mechatronics, sensors and actuators components are provided as a service. The results provided suggests that, with the use of similar recursive and distributed form of computing, production bottlenecks or fault detection can be scaled to address the complex and pervasive cyber-physical systems problems that characterize the 4th industrial revolution strategies.

In this paper we present a computational intelligence approach to solve the optimal sizing problem of grid connected microgrid (MG) components. A simulation model has been built for the MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of the long term economic benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using population based optimization techniques. We tested four different algorithms in their basic form, i.e. artificial bee colonies, particle swarm optimization, genetic algorithm and gravitational search algorithm, comparing their performances. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.

Simulators for Wireless Sensor Networks (WSNs) are one of the most important tools for systems development. They enable to study and evaluate new theories and hypotheses for sensors data gathering, testing new applications and protocols. Nowadays, there are a large number of open source WSN simulators and they can be divided into different categories according to their features and main applications. Due to the ability to increase the real WSN prototyping, the Cross Levels Simulator, like Cooja, has become an important class of simulators. Although they are open source, flexible and extensible in all levels, the test interface, the external connection at a physical level and the direct interaction with the process control via the WSN is very poor. In this work we present the Cooja Advanced Sky Interface which is an extension of the Contiki's Cooja network simulator for the Sky mote. Due to the absence of the analog output control in the Contiki OS for the Sky mote, as additional contribution, the Contiki Sky DAC driver has been developed and tested in the Cooja Simulator with the Advanced Sky GUI and GISOO plugin to give the ability to implement control over the wireless sensor network.

In recent years the European Union and, moreover, Italy has seen a rapid growth in the photovoltaic (PV) sector, following the introduction of the feed in tariff (FIT) scheme known as Conto Energia. In July 2013 the Italian government definitively cut FITs, leaving only tax benefits and a revised net metering scheme (known as "Scambio sul Posto") for new PV installations. In this scenario, the design of a new PV plant ensuring savings on electricity bills is strongly related to household electricity consumption patterns. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Using as inputs patterns of active occupancy and typical domestic habits, the fuzzy model give as output the likelihood to start each appliance within the next minute. The focus of this work is the use of this novel fuzzy model to correctly size a residential photovoltaic plant and evaluate the economic benefits of energy management actions in a case study. A cost benefits analysis is presented to quantify its effectiveness in the new net metering Italian scenario.

In the contest of household energy management, a growing interest is addressed to smart system development, able to monitor and manage resources in order to minimize wasting. One of the key factors in curbing energy consumption in the household sector is the amendment of occupant erroneous behaviours and systems malfunctioning, due to the lack of awareness of the final user. Indeed the benefits achievable with energy efficiency could be either amplified or neutralized by, respectively, good or bad practices carried out by the final users. Authors propose a diagnostic system for home energy management application able to detect faults and occupant behaviours. In particular a nonlinear monitoring method, based on Kernel Canonical Variate Analysis, is developed. To remove the assumption of normality, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method is applied to smart home temperature sensors to detect anomalies respect to efficient user behaviours and sensors and actuators faults. The method is tested on experimental data acquired in a real apartment.

This paper presents a high-resolution model of domestic electricity use, based on Fuzzy Logic Inference System (FIS). The model is built with a “bottom-up” approach and the basic block is the single appliance. Using as inputs patterns of active occupancy (i.e. when people are at home and awake) and typical domestic habits (i.e. start frequency of some appliances), the FIS model give as output the starting probability of each appliance. A post processor enable the appliances start in order to create a one-min resolution electricity demand data. In order to validate the model, electricity demand was recorded over the period of one year within 12 dwellings in the central east coast of Italy. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics.

In this paper the prototype of a novel photovoltaic and water heating system (PVT) is designed. A dynamic model of the collector based on the energy transfer phenomenon has been derived. Experimental tests on the prototype of the PVT collector have been used to validate the model under the effect of water mass flow rate and to show the performance improvement with respect a traditional photovoltaic panel. In the end a Fuzzy control scheme is proposed to maximize the performances of the collector and the results have been compared with a traditional PID controller.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

The present paper proposes an auditory BCI paradigm for systems based on P300 signals which are generated by auditory stimuli characterized by different sound typologies and locations. A Head Related Transfer Function approach is adopted to virtualize auditory stimuli. When virtualized audio is used, the user has to focus the attention both on the type and location of the stimulus, thus generating P300 signals whose amplitude is higher than that generated without audio virtualization. Classification is performed by Support Vector Machines in which gaussian radial basis functions are used as kernel functions. The system has been validated with 14 users, who were asked to choose one among five common spoken words, previously virtualized and transmitted to stereophonic headphones. Classification results prove that the proposed auditory BCI system performed similarly to common visual BCI P300 systems, representing then an alternative to visual BCI for users with visual impairments.

This paper presents a discrete-time sliding mode control based on neural networks designed for robotic manipulators. Radial basis function neural networks are used to learn about uncertainties affecting the system. The online learning algorithm combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks. A method to improve the run-time performance for the real-time implementation of the learning algorithm has been considered. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies, disturbances and payload perturbations.

In paper mill plants, the competition for increasing efficiency and reducing costs is a primary purpose. Fault detection and diagnosis can help by minimize the loss of production. In particular for the stock preparation sub-process a signal based fault detection and isolation procedure is developed. Multi-Scale Principal Component Analysis (MSPCA) is used to monitor some critical variables of the stock preparation of a paper mill plant in order to diagnose faults and malfunctions. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (Wavelet approach). The advantage of MSPCA is validated on considered paper mill plant where several sensors are installed to control and monitor the automation system.

This paper presents a discrete-time sliding mode control based on prediction compensation of uncertainties for planar robotic manipulators. Autoregressive models, identified on-line by Kalman Filters, are used to learn about uncertainties affecting the system. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies.

This paper presents theoretical details of a model-based sensor fault detection and isolation system (SFDIS) applied to unmanned ground vehicles (UGVs). Structural analysis is applied to the nonlinear model of the vehicle for residual generation. Two different solutions have been proposed for developing the residual evaluation module. The vehicle sensor suite includes a global positioning system (GPS) antenna, an inertial measurement unit (IMU), and two incremental optical encoders.

This paper presents implementation details of a model-based sensor fault detection and isolation system (SFDIS) applied to unmanned ground vehicles (UGVs). Structural analysis, applied to the nonlinear model of the UGV, is followed to build the residual generation module, followed by a residual evaluation module capable of detecting single and multiple sensor faults, as detailed in part I (Monteriu et al., 2007). The overall proposed sensor fault detection and isolation system has been tested in real-time on the ATRV-Jr mobile robot when following different trajectories in an outdoors environment. The robot sensor suite includes a global positioning system (GPS) antenna, an inertial measurement unit (IMU), and two incremental optical encoders

This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process cannot be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and the stability of this multi-controller system is analyzed using the Lyapunov theory. The performance of the switched controller is evaluated by numerical simulations

In this paper, we present a set of robust and efficient algorithms with O(N) cost for the following situations: object detection with a laser ranger; mobile robot pose estimation and a FastSLAM improved implementation. Objected detection is mainly based on a novel multiple line fitting method, related with walls at the environment. This method assumes that walls at the environment constitute a regular constrained angles. A line-based pose estimation method is also proposed, based on least-squares (LS). This method performs the matching of detected lines and estimated map lines and it can provide the global pose estimation under assumption of known data-association. FastSLAM 1.0 has been improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution. This approach has a lower computational cost than EKF approach in FastSLAM 2.0. The three algorithms have been combined in order to perform an efficient self-localization and map building process, tested for indoor environments with real data. And results show that the ideas proposed in this paper could aim in closing the loop and also to improve the overall estimation performance

In this paper the localization and environment feature estimation problems are formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and sonar measures. As gyroscopic measures are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF.

This paper explains how to associate a rigorous probability value to the main straight line features extracted from a digital image. A Bayesian approach to the Hough Transform (HT) is considered. Under general conditions, it is shown that a probability measure is associated to each line extracted from the HT. The proposed method increments the HT accumulator in a probabilistic way: first calculating the uncertainty of each edge point in the image and then using a Bayesian probabilistic scheme for fusing the probability of each edge point and calculating the line feature probability.

A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an online adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.

A navigation module for technological aids for the disabled user is presented. Usability and acceptability criteria are considered in the design of this module. Different levels of autonomy for the navigation module are considered for allowing an active interaction of the user with the technological aids. A standardized protocol for the integration of input-output devices for robotic assisted systems is also used. The navigation module is tested on an powered wheelchair and an autonomous mobile base. The sonar sensors are used for online detection of possible obstacle collisions. The reliability of sonar readings is increased by the use of a probabilistic map of the environment to support the decisions on obstacle detection.